jmlr_2016.json

[{"On the complexity of best-arm identification in multi-armed bandit models": ["Emilie Kaufmann", "Olivier Capp\u00e9", "Aur\u00e9lien Garivier"], "Multiscale dictionary learning: non-asymptotic bounds and robustness": ["Mauro Maggioni", "Stanislav Minsker", "Nate Strawn"], "Consistent algorithms for clustering time series": ["Azadeh Khaleghi", "Daniil Ryabko", "J\u00e9r\u00e9mie Mary", "Philippe Preux"], "Random rotation ensembles": ["Rico Blaser", "Piotr Fryzlewicz"], "Should we really use post-hoc tests based on mean-ranks?": ["Alessio Benavoli", "Giorgio Corani", "Francesca Mangili"], "Minimax rates in permutation estimation for feature matching": ["Olivier Collier", "Arnak S. Dalalyan"], "Consistency and fluctuations for stochastic gradient Langevin dynamics": ["Yee Whye Teh", "Alexandre H. Thiery", "Sebastian J. Vollmer"], "Knowledge matters: importance of prior information for optimization": ["\u00c7a\u01e7lar G\u00fcl\u00e7ehre", "Yoshua Bengio"], "Harry: a tool for measuring string similarity": ["Konrad Rieck", "Christian Wressnegger"], "Herded gibbs sampling": ["Yutian Chen", "Luke Bornn", "Nando De Freitas"], "Complexity of representation and inference in compositional models with part sharing": ["Alan Yuille", "Roozbeh Mottaghi"], "Noisy sparse subspace clustering": ["Yu-Xiang Wang", "Huan Xu"], "Learning the variance of the reward-to-go": ["Aviv Tamar", "Dotan Di Castro", "Shie Mannor"], "Convex calibration dimension for multiclass loss matrices": ["Harish G. Ramaswamy", "Shivani Agarwal"], "LLORMA: local low-rank matrix approximation": ["Joonseok Lee", "Seungyeon Kim", "Guy Lebanon", "Yoram Singer", "Samy Bengio"], "A consistent information criterion for support vector machines in diverging model spaces": ["Xiang Zhang", "Yichao Wu", "Lan Wang", "Runze Li"], "Extremal mechanisms for local differential privacy": ["Peter Kairouz", "Sewoong Oh", "Pramod Viswanath"], "Loss minimization and parameter estimation with heavy tails": ["Daniel Hsu", "Sivan Sabato"], "Analysis of classification-based policy iteration algorithms": ["Alessandro Lazaric", "Mohammad Ghavamzadeh", "R\u00e9mi Munos"], "Operator-valued kernels for learning from functional response data": ["Hachem Kadri", "Emmanuel Duflos", "Philippe Preux", "St\u00e9phane Canu", "Alain Rakotomamonjy", "Julien Audiffren"], "Meka: a multi-label/multi-target extension to weka": ["Jesse Read", "Peter Reutemann", "Bernhard Pfahringer", "Geoff Holmes"], "Gradients weights improve regression and classification": ["Samory Kpotufe", "Abdeslam Boularias", "Thomas Schultz", "Kyoungok Kim"], "A closer look at adaptive regret": ["Dmitry Adamskiy", "Wouter M. Koolen", "Alexey Chernov", "Vladimir Vovk"], "Learning using anti-training with sacrificial data": ["Michael L. Valenzuela", "Jerzy W. Rozenblit"], "A unifying framework in vector-valued reproducing kernel Hilbert spaces for manifold regularization and co-regularized multi-view learning": ["H\u00e0 Quang Minh", "Loris Bazzani", "Vittorio Murino"], "Quantifying uncertainty in random forests via confidence intervals and hypothesis tests": ["Lucas Mentch", "Giles Hooker"], "Statistical-computational tradeoffs in planted problems and submatrix localization with a growing number of clusters and submatrices": ["Yudong Chen", "Jiaming Xu"], "Non-linear causal inference using Gaussianity measures": ["Daniel Hern\u00e1ndez-Lobato", "Pablo Morales-Mombiela", "David Lopez-Paz", "Alberto Su\u00e1rez"], "Consistent distribution-free K-sample and independence tests for univariate random variables": ["Ruth Heller", "Yair Heller", "Shachar Kaufman", "Barak Brill"], "A Gibbs sampler for learning DAGs": ["Robert J. B. Goudie", "Sach Mukherjee"], "Dimension-free concentration bounds on hankel matrices for spectral learning": ["Fran\u00e7ois Denis", "Mattias Gybels", "Amaury Habrard"], "Distinguishing cause from effect using observational data: methods and benchmarks": ["Joris M. Mooij", "Jonas Peters", "Dominik Janzing", "Jakob Zscheischler", "Bernhard Sch\u00f6lkopf"], "Multi-task sparse structure learning with Gaussian copula models": ["Andr\u00e9 R. Gon\u00e7alves", "Fernando J. Von Zuben", "Arindam Banerjee"], "MLlib: machine learning in apache spark": ["Xiangrui Meng", "Joseph Bradley", "Burak Yavuz", "Evan Sparks", "Shivaram Venkataraman", "Davies Liu", "Jeremy Freeman", "DB Tsai", "Manish Amde", "Sean Owen", "Doris Xin", "Reynold Xin", "Michael J. Franklin", "Reza Zadeh", "Matei Zaharia", "Ameet Talwalkar"], "OLPS: a toolbox for on-line portfolio selection": ["Bin Li", "Doyen Sahoo"], "A bounded p-norm approximation of max-convolution for sub-quadratic Bayesian Inference on additive factors": ["Julianus Pfeuffer", "Oliver Serang"], "Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks": ["Shiliang Zhang", "Hui Jiang", "Lirong Dai"], "The optimal sample complexity OF PAC learning": [], "End-to-end training of deep visuomotor policies": ["Sergey Levine", "Chelsea Finn", "Trevor Darrell", "Pieter Abbeel"], "On quantile regression in reproducing kernel Hilbert spaces with the data sparsity constraint": ["Chong Zhang", "Yufeng Liu", "Yichao Wu"], "BayesPy: variational Bayesian inference in Python": ["Jaakko Luttinen"], "Variational inference for latent variables and uncertain inputs in Gaussian processes": ["Andreas C. Damianou", "Michalis K. Titsias", "Neil D. Lawrence"], "On the estimation of the gradient lines of a density and the consistency of the mean-shift algorithm": ["Ery Arias-Castro", "David Mason", "Bruno Pelletier"], "Scalable learning of Bayesian network classifiers": ["Ana M. Mart\u00ednez", "Geoffrey I. Webb", "Shenglei Chen", "Nayyar A. Zaidi"], "A unified view on multi-class support vector classification": ["\u00dcr\u00fcn Do\u01e7an", "Tobias Glasmachers", "Christian Igel"], "Addressing environment non-stationarity by repeating Q-learning updates": ["Sherief Abdallah", "Michael Kaisers"], "Large scale online kernel learning": ["Jing Lu", "Steven C. H. Hoi", "Jialei Wang", "Peilin Zhao", "Zhi-Yong Liu"], "Kernel mean shrinkage estimators": ["Krikamol Muandet", "Bharath Sriperumbudur", "Kenji Fukumizu", "Arthur Gretton", "Bernhard Sch\u00f6lkopf"], "SPSD matrix approximation vis column selection: theories, algorithms, and extensions": ["Shusen Wang", "Luo Luo", "Zhihua Zhang"], "Combinatorial multi-armed bandit and its extension to probabilistically triggered arms": ["Wei Chen", "Yajun Wang", "Yang Yuan", "Qinshi Wang"], "Differentially private data releasing for smooth queries": ["Ziteng Wang", "Chi Jin", "Kai Fan", "Jiaqi Zhang", "Junliang Huang", "Yiqiao Zhong", "Liwei Wang"], "Subspace learning with partial information": ["Alon Gonen", "Dan Rosenbaum", "Yonina C. Eldar", "Shai Shalev-Shwartz"], "Iterative hessian sketch: fast and accurate solution approximation for constrained least-squares": ["Mert Pilanci", "Martin J. Wainwright"], "Estimating causal structure using conditional DAG models": ["Chris. J. Oates", "Jim Q. Smith", "Sach Mukherjee"], "Adaptive lasso and group-lasso for functional Poisson regression": ["St\u00e9phane Ivanoff", "Franck Picard", "Vincent Rivoirard"], "Causal inference through a witness protection program": ["Ricardo Silva", "Robin Evans"], "Structure discovery in Bayesian networks by sampling partial orders": ["Teppo Niinim\u00e4ki", "Pekka Parviainen", "Mikko Koivisto"], "Estimation from pairwise comparisons: sharp minimax bounds with topology dependence": ["Nihar B. Shah", "Sivaraman Balakrishnan", "Joseph Bradley", "Abhay Parekh", "Kannan Ramchandran", "Martin J. Wainwright"], "Domain-adversarial training of neural networks": ["Yaroslav Ganin", "Evgeniya Ustinova", "Hana Ajakan", "Pascal Germain", "Hugo Larochelle", "Fran\u00e7ois Laviolette", "Mario Marchand", "Victor Lempitsky"], "Probabilistic low-rank matrix completion from quantized measurements": ["Sonia A. Bhaskar"], "DSA: decentralized double stochastic averaging gradient algorithm": ["Aryan Mokhtari", "Alejandro Ribeiro"], "The statistical performance of collaborative inference": ["G\u00e9rard Biau", "Kevin Bleakley", "Beno\u00eet Cadre"], "Convergence of an alternating maximization procedure": ["Andreas Andresen", "Vladimir Spokoiny"], "StructED: risk minimization in structured prediction": ["Yossi Adi", "Joseph Keshet"], "Stereo matching by training a convolutional neural network to compare image patches": ["Jure \u017dbontar", "Yann LeCun"], "Bayesian policy gradient and actor-critic algorithms": ["Mohammad Ghavamzadeh", "Yaakov Engel", "Michal Valko"], "Practical kernel-based reinforcement learning": ["Andr\u00e9 M. S. Barreto", "Doina Precup", "Joelle Pineau"], "An information-theoretic analysis of Thompson sampling": ["Daniel Russo", "Benjamin Van Roy"], "Compressed Gaussian process for manifold regression": ["Rajarshi Guhaniyogi", "David B. Dunson"], "On the characterization of a class of fisher-consistent loss functions and its application to boosting": ["Matey Neykov", "Jun S. Liu", "Tianxi Cai"], "Exact inference on Gaussian graphical models of arbitrary topology using path-sums": ["P.-L. Giscard", "Z. Choo", "S. J. Thwaite", "D. Jaksch"], "Challenges in multimodal gesture recognition": ["Sergio Escalera", "Vassilis Athitsos", "Isabelle Guyon"], "An emphatic approach to the problem of off-policy temporal-difference learning": ["Richard S. Sutton", "A. Rupam Mahmood", "Martha White"], "Learning algorithms for second-price auctions with reserve": ["Mehryar Mohri", "Andr\u00e9s Mu\u00f1oz Medina"], "Distributed coordinate descent method for learning with big data": ["Peter Richt\u00e1rik", "Martin Tak\u00e1\u010d"], "Scaling-up empirical risk minimization: optimization of incomplete U-statistics": ["Stephan Cl\u00e9men\u00e7on", "Igor Colin", "Aur\u00e9lien Bellet"], "Iterative regularization for learning with convex loss functions": ["Junhong Lin", "Lorenzo Rosasco", "Ding-Xuan Zhou"], "Latent space inference of internet-scale networks": ["Qirong Ho", "Junming Yin", "Eric P. Xing"], "Patient risk stratification with time-varying parameters: a multitask learning approach": ["Jenna Wiens", "John Guttag", "Eric Horvitz"], "Multiplicative multitask feature learning": ["Xin Wang", "Jinbo Bi", "Shipeng Yu", "Jiangwen Sun", "Minghu Song"], "The benefit of multitask representation learning": ["Andreas Maurer", "Massimiliano Pontil"], "Model-free variable selection in reproducing kernel Hilbert space": ["Lei Yang", "Shaogao Lv", "Junhui Wang"], "CVXPY: a python-embedded modeling language for convex optimization": ["Steven Diamond", "Stephen Boyd"], "Lenient learning in independent-learner stochastic cooperative games": ["Ermo Wei", "Sean Luke"], "Structure-leveraged methods in breast cancer risk prediction": ["Jun Fan", "Yirong Wu", "Ming Yuan", "David Page", "Jie Liu", "Irene M. Ong", "Peggy Peissig", "Elizabeth Burnside"], "LIBMF: a library for parallel matrix factorization in shared-memory systems": ["Wei-Sheng Chin", "Bo-Wen Yuan", "Meng-Yuan Yang", "Yong Zhuang", "Yu-Chin Juan", "Chih-Jen Lin"], "L1-regularized least squares for support recovery of high dimensional single index models with Gaussian designs": ["Matey Neykov", "Jun S. Liu", "Tianxi Cai"], "Spectral ranking using seriation": ["Fajwel Fogel", "Alexandre d'Aspremont", "Milan Vojnovic"], "Sparsity and error analysis of empirical feature-based regularization schemes": ["Xin Guo", "Jun Fan", "Ding-Xuan Zhou"], "Estimating diffusion networks: recovery conditions, sample complexity & soft-thresholding algorithm": ["Manuel Gomez-Rodriguez", "Le Song", "Hadi Daneshmand", "Bernhard Sch\u00f6lkopf"], "Rounding-based moves for semi-metric labeling": ["M. Pawan Kumar", "Puneet K. Dokania"], "Rate optimal denoising of simultaneously sparse and low rank matrices": ["Dan Yang", "Zongming Ma", "Andreas Buja"], "Hierarchical relative entropy policy search": ["Christian Daniel", "Gerhard Neumann", "Oliver Kroemer", "Jan Peters"], "Convex regression with interpretable sharp partitions": ["Ashley Petersen", "Noah Simon", "Daniela Witten"], "JCLAL: a Java framework for active learning": ["Oscar Reyes", "Eduardo P\u00e9rez", "Mar\u00eda Del Carmen Rodr\u00edguez-Hern\u00e1ndez", "Habib M. Fardoun", "Sebasti\u00e1n Ventura"], "Integrated common sense learning and planning in POMDPs": ["Brendan Juba"], "Cells in multidimensional recurrent neural networks": ["Gundram Leifert", "Tobias Strau\u00df", "Tobias Gr\u00fcning", "Welf Wustlich", "Roger Labahn"], "Learning taxonomy adaptation in large-scale classification": ["Rohit Babbar", "Ioannis Partalas", "Eric Gaussier", "Massih-Reza Amini", "C\u00e9cile Amblard"], "How to center deep Boltzmann machines": ["Jan Melchior", "Asja Fischer", "Laurenz Wiskott"], "Control function instrumental variable estimation of nonlinear causal effect models": ["Zijian Guo", "Dylan S. Small"], "Structure learning in Bayesian networks of a moderate size by efficient sampling": ["Ru He", "Jin Tian", "Huaiqing Wu"], "Spectral methods meet EM: a provably optimal algorithm for crowdsourcing": ["Yuchen Zhang", "Xi Chen", "Dengyong Zhou", "Michael I. Jordan"], "Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models": ["Aki Vehtari", "Tommi Mononen", "Ville Tolvanen", "Tuomas Sivula", "Ole Winther"], "\u03b5-PAL: an active learning approach to the multi-objective optimization problem": ["Marcela Zuluaga", "Andreas Krause", "Markus P\u00fcschel"], "Trend filtering on graphs": ["Yu-Xiang Wang", "James Sharpnack", "Alexander J. Smola", "Ryan J. Tibshirani"], "Multi-task learning for straggler avoiding predictive job scheduling": ["Neeraja J. Yadwadkar", "Bharath Hariharan", "Joseph E. Gonzalez", "Randy Katz"], "Interleaved text/image deep mining on a large-scale radiology database for automated image interpretation": ["Hoo-Chang Shin", "Le Lu", "Lauren Kim", "Ari Seff", "Jianhua Yao", "Ronald M. Summers"], "Distribution-matching embedding for visual domain adaptation": ["Mahsa Baktashmotlagh", "Mehrtash Harandi", "Mathieu Salzmann"], "Monotonic calibrated interpolated look-up tables": ["Maya Gupta", "Andrew Cotter", "Jan Pfeifer", "Konstantin Voevodski", "Kevin Canini", "Alexander Mangylov", "Wojciech Moczydlowski", "Alexander Van Esbroeck"], "Are random forests truly the best classifiers?": ["Michael Wainberg", "Babak Alipanahi", "Brendan J. Frey"], "Minimax adaptive estimation of nonparametric hidden Markov models": ["Yohann De Castro", "\u00c9lisabeth Gassiat", "Claire Lacour"], "Decrypting \"cryptogenic\" epilepsy: semi-supervised hierarchical conditional random fields for detecting cortical lesions in MRI-negative patients": ["Bilal Ahmed", "Thomas Thesen", "Karen E. Blackmon", "Ruben Kuzniekcy", "Orrin Devinsky", "Carla E. Brodley"], "Fused lasso approach in regression coefficients clustering: learning parameter heterogeneity in data integration": ["Lu Tang", "Peter X .K. Song"], "The LRP toolbox for artificial neural networks": ["Sebastian Lapuschkin", "Alexander Binder", "Gr\u00e9goire Montavon", "Klaus-Robert M\u00fcller", "Wojciech Samek"], "Equivalence of graphical lasso and thresholding for sparse graphs": ["Somayeh Sojoudi"], "A network that learns strassen multiplication": ["Veit Elser"], "Revisiting the Nystr\u00f6m method for improved large-scale machine learning": ["Alex Gittens", "Michael W. Mahoney"], "Improving structure MCMC for Bayesian networks through Markov blanket resampling": ["Chengwei Su", "Mark E. Borsuk"], "Volumetric spanners: an efficient exploration basis for learning": ["Elad Hazan", "Zohar Karnin"], "Quasi-Monte Carlo feature maps for shift-invariant kernels": ["Haim Avron", "Vikas Sindhwani", "Jiyan Yang", "Michael W. Mahoney"], "Variational dependent multi-output Gaussian process dynamical systems": ["Jing Zhao", "Shiliang Sun"], "Multiple output regression with latent noise": ["Jussi Gillberg", "Pekka Marttinen", "Matti Pirinen", "Antti J. Kangas", "Pasi Soininen", "Mehreen Ali", "Aki S. Havulinna", "Marjo-Riitta J\u00e4rvelin", "Mika Ala-Korpela", "Samuel Kaski"], "The constrained dantzig selector with enhanced consistency": ["Yinfei Kong", "Zemin Zheng", "Jinchi Lv"], "Bootstrap-based regularization for low-rank matrix estimation": ["Julie Josse", "Stefan Wager"], "Bayesian optimization for likelihood-free inference of simulator-based statistical models": ["Michael U. Gutmann", "Jukka Corander"], "On lower and upper bounds in smooth and strongly convex optimization": ["Yossi Arjevani", "Shai Shalev-Shwartz", "Ohad Shamir"], "Dual control for approximate Bayesian reinforcement learning": ["Edgar D. Klenske", "Philipp Hennig"], "Multiple-instance learning from distributions": ["Gary Doran", "Soumya Ray"], "An online convex optimization approach to Blackwell's approachability": ["Nahum Shimkin"], "A well-conditioned and sparse estimation of covariance and inverse covariance matrices using a joint penalty": ["Ashwini Maurya"], "String and membrane Gaussian processes": ["Yves-Laurent Kom Samo", "Stephen J. Roberts"], "Extracting PICO sentences from clinical trial reports using supervised distant supervision": ["Byron C. Wallace", "Jo\u00ebl Kuiper", "Aakash Sharma", "Mingxi Zhu", "Iain J. Marshall"], "Cross-corpora unsupervised learning of trajectories in autism spectrum disorders": ["Huseyin Melih Elibol", "Vincent Nguyen", "Scott Linderman", "Matthew Johnson", "Amna Hashmi", "Finale Doshi-Velez"], "Adjusting for chance clustering comparison measures": ["Simone Romano", "Nguyen Xuan Vinh", "James Bailey", "Karin Verspoor"], "Refined error bounds for several learning algorithms": ["Steve Hanneke"], "Synergy of monotonic rules": ["Vladimir Vapnik", "Rauf Izmailov"], "Pymanopt: a python toolbox for optimization on manifolds using automatic differentiation": ["James Townsend", "Niklas Koep", "Sebastian Weichwald"], "CrossCat: a fully Bayesian nonparametric method for analyzing heterogeneous, high dimensional data": [], "Regularized policy iteration with nonparametric function spaces": ["Amir-massoud Farahmand", "Mohammad Ghavamzadeh", "Csaba Szepesv\u00e1ri", "Shie Mannor"], "Multiscale adaptive representation of signals: I. the basic framework": ["Cheng Tai", "E. Weinan"], "Sparse PCA via covariance thresholding": ["Yash Deshpande", "Andrea Montanari"], "Large scale visual recognition through adaptation using joint representation and multiple instance learning": ["Judy Hoffman", "Deepak Pathak", "Eric Tzeng", "Jonathan Long", "Sergio Guadarrama", "Trevor Darrell", "Kate Saenko"], "Covariance-based clustering in multivariate and functional data analysis": ["Francesca Ieva", "Anna Maria Paganoni", "Nicholas Tarabelloni"], "MOCCA: mirrored convex/concave optimization for nonconvex composite functions": ["Rina Foygel Barber", "Emil Y. Sidky"], "True online temporal-difference learning": ["Harm Van Seijen", "A. Rupam Mahmood", "Patrick M. Pilarski", "Marlos C. Machado", "Richard S. Sutton"], "Penalized maximum likelihood estimation of multi-layered Gaussian graphical models": ["Jiahe Lin", "Sumanta Basu", "Moulinath Banerjee", "George Michailidis"], "Local network community detection with continuous optimization of conductance and weighted kernel K-means": ["Twan Van Laarhoven", "Elena Marchiori"], "Megaman: scalable manifold learning in python": ["James McQueen", "Marina Meil\u0103", "Jacob VanderPlas", "Zhongyue Zhang"], "Kernel estimation and model combination in a bandit problem with covariates": ["Wei Qian", "Yuhong Yang"], "A general framework for consistency of principal component analysis": ["Dan Shen", "Haipeng Shen", "J. S. Marron"], "Conditional independencies under the algorithmic independence of conditionals": ["Jan Lemeire"], "Learning theory for distribution regression": ["Zolt\u00e1n Szab\u00f3", "Bharath K. Sriperumbudur", "Barnab\u00e1s P\u00f3czos", "Arthur Gretton"], "A differential equation for modeling Nesterov's accelerated gradient method: theory and insights": ["Weijie Su", "Stephen Boyd", "Emmanuel J. Cand\u00e8s"], "Importance weighting without importance weights: an efficient algorithm for combinatorial semi-bandits": ["Gergely Neu", "G\u00e1bor Bart\u00f3k"], "New perspectives on k-support and cluster norms": ["Andrew M. McDonald", "Massimiliano Pontil", "Dimitris Stamos"], "Minimum density hyperplanes": ["Nicos G. Pavlidis", "David P. Hofmeyr", "Sotiris K. Tasoulis"], "Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs": ["Alberto N. Escalante-B.", "Laurenz Wiskott"], "Universal approximation results for the temporal restricted Boltzmann machine and the recurrent temporal restricted Boltzmann machine": ["Simon Odense", "Roderick Edwards"], "Exploration of the (non-)asymptotic bias and variance of stochastic gradient langevin dynamics": ["Sebastian J. Vollmer", "Konstantinos C. Zygalakis", "Yee Whye Teh"], "A general framework for constrained Bayesian optimization using information-based search": ["Jos\u00e9 Miguel Hern\u00e1ndez-Lobato", "Michael A. Gelbart", "Ryan P. Adams", "Matthew W. Hoffman", "Zoubin Ghahramani"], "Optimal estimation and completion of matrices with biclustering structures": ["Chao Gao", "Yu Lu", "Zongming Ma", "Harrison H. Zhou"], "The teaching dimension of linear learners": ["Ji Liu", "Xiaojin Zhu"], "Augmentable gamma belief networks": ["Mingyuan Zhou", "Yulai Cong", "Bo Chen"], "Optimal estimation of derivatives in nonparametric regression": ["Wenlin Dai", "Tiejun Tong", "Marc G. Genton"], "Double or nothing: multiplicative incentive mechanisms for crowdsourcing": ["Nihar B. Shah", "Dengyong Zhou"], "Joint structural estimation of multiple graphical models": ["Jing Ma", "George Michailidis"], "Support vector hazards machine: a counting process framework for learning risk scores for censored outcomes": ["Yuanjia Wang", "Tianle Chen", "Donglin Zeng"], "Stable graphical models": ["Navodit Misra", "Ercan E. Kuruoglu"], "Bounding the search space for global optimization of neural networks learning error: an interval analysis approach": ["Stavros P. Adam", "George D. Magoulas", "Dimitrios A. Karras", "Michael N. Vrahatis"], "mlr: machine learning in R": ["Bernd Bischl", "Michel Lang", "Lars Kotthoff", "Julia Schiffner", "Jakob Richter", "Erich Studerus", "Giuseppe Casalicchio", "Zachary M. Jones"], "Feature-level domain adaptation": ["Wouter M. Kouw", "Laurens J. P. Van Der Maaten", "Jesse H. Krijthe", "Marco Loog"], "Semiparametric mean field variational Bayes: general principles and numerical issues": ["David Rohde", "Matt P. Wand"], "Online PCA with optimal regret": ["Jiazhong Nie", "Wojciech Kot\u0142owski", "Manfred K. Warmuth"], "Efficient computation of Gaussian process regression for large spatial data sets by patching local Gaussian processes": ["Chiwoo Park", "Jianhua Z. Huang"], "bandicoot: a python toolbox for mobile phone metadata": ["Yves-Alexandre De Montjoye", "Luc Rocher", "Alex Sandy Pentland"], "Input output kernel regression: supervised and semi-supervised structured output prediction with operator-valued kernels": ["C\u00e9line Brouard", "Marie Szafranski", "Florence D'Alch\u00e9-Buc"], "A note on the sample complexity of the Er-SpUD algorithm by Spielman, Wang and Wright for exact recovery of sparsely used dictionaries": ["Rados\u0142aw Adamczak"], "The asymptotic performance of linear echo state neural networks": ["Romain Couillet", "Gilles Wainrib", "Harry Sevi", "Hafiz Tiomoko Ali"], "On the consistency of the likelihood maximization vertex nomination scheme: bridging the gap between maximum likelihood estimation and graph matching": ["Vince Lyzinski", "Keith Levin", "Donniell E. Fishkind", "Carey E. Priebe"], "Characteristic kernels and infinitely divisible distributions": ["Yu Nishiyama", "Kenji Fukumizu"], "Consistency of cheeger and ratio graph cuts": ["Nicol\u00e1s Garc\u00eda Trillos", "Dejan Slep\u010dev", "James Von Brecht", "Thomas Laurent", "Xavier Bresson"], "Jointly informative feature selection made tractable by Gaussian modeling": ["Leonidas Lefakis", "Fran\u00e7ois Fleuret"], "Learning with differential privacy: stability, learnability and the sufficiency and necessity of ERM principle": ["Yu-Xiang Wang", "Jing Lei", "Stephen E. Fienberg"], "fastFM: a library for factorization machines": ["Immanuel Bayer"], "The factorized self-controlled case series method: an approach for estimating the effects of many drugs on any outcomes": ["Ramin Moghaddass", "Cynthia Rudin", "David Madigan"], "Electronic health record analysis via deep Poisson factor models": ["Ricardo Henao", "James T. Lu", "Joseph E. Lucas", "Jeffrey Ferranti", "Lawrence Carin"], "Low-rank doubly stochastic matrix decomposition for cluster analysis": ["Zhirong Yang", "Jukka Corander", "Erkki Oja"], "A new algorithm and theory for penalized regression-based clustering": ["Chong Wu", "Sunghoon Kwon", "Xiaotong Shen", "Wei Pan"], "Classification of imbalanced data with a geometric digraph family": ["Art\u00fcr Manukyan", "Elvan Ceyhan"], "A variational approach to path estimation and parameter inference of hidden diffusion processes": ["Tobias Sutter", "Arnab Ganguly", "Heinz Koeppl"], "One-class classification of point patterns of extremes": ["Stijn Luca", "David A. Clifton", "Bart Vanrumste"], "On the influence of momentum acceleration on online learning": ["Kun Yuan", "Bicheng Ying", "Ali H. Sayed"], "Data-driven rank breaking for efficient rank aggregation": ["Ashish Khetan", "Sewoong Oh"], "Optimal learning rates for localized SVMs": ["Mona Meister", "Ingo Steinwart"], "Bipartite ranking: a risk-theoretic perspective": ["Aditya Krishna Menon", "Robert C. Williamson"], "Bayesian group factor analysis with structured sparsity": ["Shiwen Zhao", "Chuan Gao", "Sayan Mukherjee", "Barbara E. Engelhardt"], "Machine learning in an auction environment": ["Patrick Hummel", "R. Preston McAfee"], "Wavelet decompositions of Random Forests: smoothness analysis, sparse approximation and applications": ["Oren Elisha", "Shai Dekel"], "Mutual information based matching for causal inference with observational data": ["Lei Sun", "Alexander G. Nikolaev"], "Online trans-dimensional von Mises-Fisher mixture models for user profiles": ["Xiangju Qin", "P\u00e1draig Cunningham", "Michael Salter-Townshend"], "Multivariate spearman's \u03c1 for aggregating ranks using copulas": ["Justin Bed\u00f6", "Cheng Soon Ong"], "Nonparametric network models for link prediction": ["Sinead A. Williamson"], "Guarding against spurious discoveries in high dimensions": ["Jianqing Fan", "Wen-Xin Zhou"], "Bayesian graphical models for multivariate functional data": ["Hongxiao Zhu", "Nate Strawn", "David B. Dunson"], "Neural autoregressive distribution estimation": ["Benigno Uria", "Marc-Alexandre C\u00f4t\u00e9", "Karol Gregor", "Iain Murray", "Hugo Larochelle"], "ERRATA: on the estimation of the gradient lines of a density and the consistency of the mean-shift algorithm": ["Ery Arias-Castro", "David Mason", "Bruno Pelletier"], "Modelling interactions in high-dimensional data with backtracking": ["Rajen D. Shah"], "Choice of V for V-fold cross-validation in least-squares density estimation": ["Sylvain Arlot", "Matthieu Lerasle"], "Towards more efficient SPSD matrix approximation and CUR matrix decomposition": ["Shusen Wang", "Zhihua Zhang", "Tong Zhang"], "Multi-objective Markov decision processes for data-driven decision support": ["Daniel J. Lizotte", "Eric B. Laber"], "Measuring dependence powerfully and equitably": ["Yakir A. Reshef", "David N. Reshef", "Hilary K. Finucane", "Pardis C. Sabeti", "Michael Mitzenmacher"], "Neyman-Pearson classification under high-dimensional settings": ["Anqi Zhao", "Yang Feng", "Lie Wang", "Xin Tong"], "A statistical perspective on randomized sketching for ordinary least-squares": ["Garvesh Raskutti", "Michael W. Mahoney"], "Learning planar ising models": ["Jason K. Johnson", "Diane Oyen", "Michael Chertkov", "Praneeth Netrapalli"], "Newton-stein method: an optimization method for GLMs via Stein's lemma": ["Murat A. Erdogdu"], "Bayesian decision process for cost-efficient dynamic ranking via crowdsourcing": ["Xi Chen", "Kevin Jiao", "Qihang Lin"], "Multi-scale classification using localized spatial depth": ["Subhajit Dutta", "Soham Sarkar", "Anil K. Ghosh"], "On Bayes risk lower bounds": ["Xi Chen", "Adityanand Guntuboyina", "Yuchen Zhang"], "Weak convergence properties of constrained emphatic temporal-difference learning with constant and slowly diminishing stepsize": ["Huizhen Yu"], "RLScore: regularized least-squares learners": ["Tapio Pahikkala", "Antti Airola"], "Stability and generalization in structured prediction": ["Ben London", "Bert Huang", "Lise Getoor"], "Composite multiclass losses": ["Robert C. Williamson", "Elodie Vernet", "Mark D. Reid"], "Learning latent variable models by pairwise cluster comparison Part I: theory and overview": ["Nuaman Asbeh", "Boaz Lerner"], "GenSVM: a generalized multiclass support vector machine": ["Gerrit J. J. Van Den Burg", "Patrick J. F. Groenen"], "Scalable approximate Bayesian inference for outlier detection under informative sampling": ["Terrance D. Savitsky"], "Approximate Newton methods for policy search in Markov decision processes": ["Thomas Furmston", "Guy Lever", "David Barber"], "Gains and losses are fundamentally different in regret minimization: the sparse case": ["Joon Kwon", "Vianney Perchet"], "Linear convergence of randomized feasible descent methods under the weak strong convexity assumption": ["Chenxin Ma", "Rachael Tappenden", "Martin Tak\u00e1\u010d"], "A practical scheme and fast algorithm to tune the lasso with optimality guarantees": ["Micha\u00ebl Chichignoud", "Johannes Lederer", "Martin J. Wainwright"], "A characterization of linkage-based hierarchical clustering": ["Margareta Ackerman", "Shai Ben-David"], "Learning latent variable models by pairwise cluster comparison Part II: algorithm and evaluation": ["Nuaman Asbeh", "Boaz Lerner"], "Integrative analysis using coupled latent variable models for individualizing prognoses": ["Peter Schulam", "Suchi Saria"], "An error bound for L1-norm support vector machine coefficients in ultra-high dimension": ["Bo Peng", "Lan Wang", "Yichao Wu"], "Blending learning and inference in conditional random fields": ["Tamir Hazan", "Alexander G. Schwing", "Raquel Urtasun"], "Distributed submodular maximization": ["Baharan Mirzasoleiman", "Amin Karbasi", "Rik Sarkar", "Andreas Krause"], "On the properties of variational approximations of Gibbs posteriors": ["Pierre Alquier", "James Ridgway", "Nicolas Chopin"]}, {"On the complexity of best-arm identification in multi-armed bandit models": ["best-arm identification", "information-theoretic divergences", "multi-armed bandit", "pure exploration", "sequential testing"], "Multiscale dictionary learning: non-asymptotic bounds and robustness": ["dictionary learning", "manifold learning", "multi-resolution analysis", "robustness", "sparsity"], "Consistent algorithms for clustering time series": ["clustering", "ergodicity", "time series", "unsupervised learning"], "Random rotation ensembles": ["ensemble diversity", "feature rotation", "smooth decision boundary"], "Should we really use post-hoc tests based on mean-ranks?": ["friedman test", "post-hoc test", "statistical comparison"], "Minimax rates in permutation estimation for feature matching": ["feature matching", "minimax rate of separation", "permutation estimation"], "Consistency and fluctuations for stochastic gradient Langevin dynamics": ["big data", "langevin dynamics", "markov chain monte carlo"], "Knowledge matters: importance of prior information for optimization": ["curriculum learning", "deep learning", "evolution of culture", "neural networks", "optimization", "training with hints"], "Harry: a tool for measuring string similarity": ["similarity measures for strings", "string distances", "string kernels"], "Herded gibbs sampling": ["deterministic sampling", "gibbs sampling", "herding"], "Complexity of representation and inference in compositional models with part sharing": ["compositional models", "hierarchical architectures", "object detection", "part sharing"], "Noisy sparse subspace clustering": ["compressive sensing", "robustness", "sparse", "stability", "subspace clustering"], "Learning the variance of the reward-to-go": ["markov decision processes", "reinforcement learning", "simulation", "temporal differences", "variance estimation"], "Convex calibration dimension for multiclass loss matrices": ["calibrated surrogates", "classification calibration", "convex surrogates", "loss matrix", "multiclass loss", "statistical consistency", "subset ranking", "surrogate loss"], "LLORMA: local low-rank matrix approximation": ["collaborative filtering", "kernel smoothing", "matrix approximation", "non-parametric methods", "recommender systems"], "A consistent information criterion for support vector machines in diverging model spaces": ["bayesian information criterion", "diverging model spaces", "feature selection", "support vector machines"], "Extremal mechanisms for local differential privacy": ["estimation", "f-divergences", "hypothesis testing", "information theoretic utilities", "local differential privacy", "mutual information", "privacy-preserving machine learning algorithms", "statistical inference"], "Loss minimization and parameter estimation with heavy tails": ["heavy-tailed distributions", "least squares", "linear regression", "unbounded losses"], "Analysis of classification-based policy iteration algorithms": ["classification-based approach to policy iteration", "finite-sample analysis", "policy iteration", "reinforcement learning"], "Operator-valued kernels for learning from functional response data": ["audio signal processing", "function-valued reproducing kernel hilbert spaces", "nonlinear functional data analysis", "operator-valued kernels"], "Meka: a multi-label/multi-target extension to weka": ["classification", "incremental", "learning", "multi-label", "multi-target"], "Gradients weights improve regression and classification": ["feature selection", "feature weighting", "metric learning", "nonparametric learning", "nonparametric sparsity"], "A closer look at adaptive regret": ["adaptive regret", "fixed share", "online learning", "specialist experts"], "Learning using anti-training with sacrificial data": ["anti-training", "machine learning", "meta optimization", "no free lunch", "optimization", "sacrificial data"], "A unifying framework in vector-valued reproducing kernel Hilbert spaces for manifold regularization and co-regularized multi-view learning": ["kernel methods", "manifold regularization", "multi-class classification", "multi-kernel learning", "multi-modality learning", "multi-view learning", "vector-valued rkhs"], "Quantifying uncertainty in random forests via confidence intervals and hypothesis tests": ["bagging", "random forests", "subbagging", "trees", "u-statistics"], "Statistical-computational tradeoffs in planted problems and submatrix localization with a growing number of clusters and submatrices": ["bi-clustering", "computational hardness", "convex relaxation", "graph clustering", "minimax recovery", "planted clique", "planted coloring", "planted partition", "submatrix localization"], "Non-linear causal inference using Gaussianity measures": ["causal inference", "cause-effect pairs", "gaussianity of the residuals"], "Consistent distribution-free K-sample and independence tests for univariate random variables": ["bivariate distribution", "hhg r package", "mutual information", "nonparametric test", "statistical independence", "two-sample test"], "A Gibbs sampler for learning DAGs": ["bayesian networks", "dags", "gibbs sampling", "markov chain monte carlo", "structure learning", "variable selection"], "Dimension-free concentration bounds on hankel matrices for spectral learning": ["hankel matrices", "matrix bernstein bounds", "probabilistic grammatical inference", "rational series", "spectral learning"], "Distinguishing cause from effect using observational data: methods and benchmarks": ["additive noise", "benchmarks", "causal discovery", "cause-effect pairs", "information-geometric causal inference"], "Multi-task sparse structure learning with Gaussian copula models": ["gaussian copula", "multi-task learning", "probabilistic graphical model", "sparse modeling", "structure learning"], "MLlib: machine learning in apache spark": ["apache spark", "distributed algorithms", "scalable machine learning"], "OLPS: a toolbox for on-line portfolio selection": ["on-line portfolio selection", "online learning", "simulation", "trading system"], "A bounded p-norm approximation of max-convolution for sub-quadratic Bayesian Inference on additive factors": ["bayesian inference", "fast fourier transform", "hidden markov model", "lp space", "max-convolution", "maximum a posteriori", "null space projection", "p-norm", "polynomial matrix"], "Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks": ["mixture models", "neural networks", "orthogonal projection", "pca", "unsupervised learning", "von mises-fisher model"], "The optimal sample complexity OF PAC learning": ["learning algorithm", "minimax analysis", "pac learning", "sample complexity", "statistical learning theory"], "End-to-end training of deep visuomotor policies": ["neural networks", "optimal control", "reinforcement learning", "vision"], "On quantile regression in reproducing kernel Hilbert spaces with the data sparsity constraint": ["kernel learning", "rademacher complexity", "regression", "smoothing", "sparsity"], "BayesPy: variational Bayesian inference in Python": ["probabilistic programming", "python", "variational bayes"], "Variational inference for latent variables and uncertain inputs in Gaussian processes": ["dynamical systems", "gaussian processes", "latent variable models", "uncertain inputs", "variational inference"], "On the estimation of the gradient lines of a density and the consistency of the mean-shift algorithm": ["density estimation", "gradient lines", "mean-shift", "nonparametric clustering"], "Scalable learning of Bayesian network classifiers": ["big data", "feature selection", "out-of-core learning", "scalable bayesian classification"], "A unified view on multi-class support vector classification": ["consistency", "multi-class classification", "support vector machines"], "Addressing environment non-stationarity by repeating Q-learning updates": ["multi-agent learning", "non-stationary environments", "q-learning", "reinforcement learning"], "Large scale online kernel learning": ["kernel approximation", "large scale machine learning", "online learning"], "Kernel mean shrinkage estimators": ["covariance operator", "james-stein estimators", "kernel mean", "kernel methods", "shrinkage estimators", "stein effect", "tikhonov regularization"], "SPSD matrix approximation vis column selection: theories, algorithms, and extensions": ["kernel methods", "matrix approximation", "matrix factorization", "spectral shifting", "the nystr\u00f6m method"], "Combinatorial multi-armed bandit and its extension to probabilistically triggered arms": ["combinatorial multi-armed bandit", "online advertising", "online learning", "social influence maximization", "upper confidence bound"], "Differentially private data releasing for smooth queries": ["differential privacy", "smooth queries", "synthetic dataset"], "Subspace learning with partial information": ["budgeted learning", "learning theory", "learning with partial information", "principal components analysis", "statistical learning"], "Iterative hessian sketch: fast and accurate solution approximation for constrained least-squares": ["convex optimization", "information theory", "lasso", "low-rank approximation", "random projection"], "Estimating causal structure using conditional DAG models": ["causal inference", "data integration", "directed acyclic graphs", "graphical models", "instrumental variables"], "Adaptive lasso and group-lasso for functional Poisson regression": ["adaptive group-lasso", "adaptive lasso", "calibration", "concentration", "functional poisson regression"], "Causal inference through a witness protection program": ["bayesian inference", "causal inference", "instrumental variables", "linear programming"], "Structure discovery in Bayesian networks by sampling partial orders": ["annealed importance sampling", "directed acyclic graph", "fast zeta transform", "linear extension", "markov chain monte carlo"], "Estimation from pairwise comparisons: sharp minimax bounds with topology dependence": ["crowdsourcing", "graph", "pairwise comparisons", "ranking", "topology"], "Domain-adversarial training of neural networks": ["deep learning", "domain adaptation", "image classification", "neural network", "person re-identification", "representation learning", "sentiment analysis", "synthetic data"], "Probabilistic low-rank matrix completion from quantized measurements": ["collaborative filtering", "constrained maximum likelihood", "convex optimization", "matrix completion", "quantization"], "DSA: decentralized double stochastic averaging gradient algorithm": ["decentralized optimization", "large-scale optimization", "linear convergence", "logistic regression", "stochastic averaging gradient", "stochastic optimization"], "The statistical performance of collaborative inference": ["collaborative estimation", "complexity", "distributed computing", "graph theory", "ramanujan graph", "stochastic matrix"], "Convergence of an alternating maximization procedure": ["alternating maximization", "alternating minimization", "em-algorithm", "local concentration", "local linear approximation", "m-estimation", "profile maximum likelihood", "semiparametric"], "StructED: risk minimization in structured prediction": ["crf", "direct loss minimization", "structural svm", "structured prediction"], "Stereo matching by training a convolutional neural network to compare image patches": ["convolutional neural networks", "matching cost", "similarity learning", "stereo", "supervised learning"], "Bayesian policy gradient and actor-critic algorithms": ["actor-critic algorithms", "bayesian inference", "gaussian processes", "policy gradient methods", "reinforcement learning"], "Practical kernel-based reinforcement learning": ["dynamic programming", "kernel-based approximation", "markov decision processes", "reinforcement learning", "stochastic factorization"], "An information-theoretic analysis of Thompson sampling": ["information theory", "mutli-armed bandit", "online optimization", "regret bounds", "thompson sampling"], "Compressed Gaussian process for manifold regression": ["compressed regression", "gaussian process", "gaussian random projection", "large p", "manifold regression"], "On the characterization of a class of fisher-consistent loss functions and its application to boosting": ["boosting", "fisher-consistency", "multiclass classification", "samme"], "Exact inference on Gaussian graphical models of arbitrary topology using path-sums": ["belief propagation", "block matrices", "gaussian graphical models", "graphs of arbitrary topology", "path-sum", "walk-sum"], "Challenges in multimodal gesture recognition": ["computer vision", "gesture recognition", "infrared cameras", "kinect\u2122", "multimodal data analysis", "pattern recognition", "time series analysis", "wearable sensors"], "An emphatic approach to the problem of off-policy temporal-difference learning": ["convergence", "function approximation", "off-policy learning", "stability", "temporal-difference learning"], "Learning algorithms for second-price auctions with reserve": ["auctions", "learning theory", "revenue optimization"], "Distributed coordinate descent method for learning with big data": ["boosting", "distributed algorithms", "parallel coordinate descent", "stochastic methods"], "Scaling-up empirical risk minimization: optimization of incomplete U-statistics": ["big data", "empirical risk minimization", "rate bound analysis", "sampling design", "stochastic gradient descent", "u-processes"], "Iterative regularization for learning with convex loss functions": [], "Latent space inference of internet-scale networks": ["big data", "distributed computation", "probabilistic network models", "stochastic variational inference", "triangular modeling"], "Patient risk stratification with time-varying parameters: a multitask learning approach": ["clostridium difficile", "healthcare-associated infections", "multitask learning", "risk stratification", "time-varying coefficients"], "Multiplicative multitask feature learning": ["blockwise coordinate descent", "multitask learning", "regularization", "sparse modeling"], "The benefit of multitask representation learning": ["learning-to-learn", "multitask learning", "representation learning", "statistical learning theory", "transfer learning"], "Model-free variable selection in reproducing kernel Hilbert space": ["group lasso", "high-dimensional data", "kernel regression", "learning gradients", "reproducing kernel hilbert space", "variable selection"], "CVXPY: a python-embedded modeling language for convex optimization": ["conic programming", "convex optimization", "convexity verification", "domain-specific languages", "python"], "Lenient learning in independent-learner stochastic cooperative games": ["game theory", "independent learner", "lenient learning", "multiagent learning", "reinforcement learning"], "Structure-leveraged methods in breast cancer risk prediction": ["breast cancer risk prediction", "genetic variants", "mammography descriptors", "personalized medicine", "structure information"], "LIBMF: a library for parallel matrix factorization in shared-memory systems": ["adaptive learning rate", "binary matrix factorization", "logistic matrix factorization", "matrix factorization", "non-negative matrix factorization", "one-class matrix factorization", "parallel computation", "stochastic gradient method"], "L1-regularized least squares for support recovery of high dimensional single index models with Gaussian designs": ["high-dimensional statistics", "lasso", "single index models", "sparsity", "support recovery"], "Spectral ranking using seriation": ["ranking", "seriation", "spectral methods"], "Sparsity and error analysis of empirical feature-based regularization schemes": ["concave regularizer", "lq-penalty", "regularization with empirical features", "reproducing kernel hilbert space", "scad penalty", "sparsity"], "Estimating diffusion networks: recovery conditions, sample complexity & soft-thresholding algorithm": [], "Rounding-based moves for semi-metric labeling": ["linear programming relaxation", "move-making algorithms", "multiplicative bounds", "semi-metric labeling"], "Rate optimal denoising of simultaneously sparse and low rank matrices": ["denoising", "high dimensionality", "low rank matrices", "minimax rates", "simultaneously structured matrices", "sparse svd", "sparsity"], "Hierarchical relative entropy policy search": ["hierarchical learning", "hireps", "motor skill learning", "policy search", "reinforcement learning", "reps", "robot learning", "robust learning", "structured learning", "temporal correlation"], "Convex regression with interpretable sharp partitions": ["convex optimization", "interpretability", "non-additivity", "non-parametric regression", "prediction"], "JCLAL: a Java framework for active learning": ["active learning", "framework", "java language", "object-oriented design"], "Integrated common sense learning and planning in POMDPs": ["decision tree policies", "noisy strips", "non-monontonic reasoning", "pac-semantics", "partially observed markov decision process"], "Cells in multidimensional recurrent neural networks": ["ctc", "handwriting recognition", "lstm", "mdrnn", "neural network"], "Learning taxonomy adaptation in large-scale classification": ["hierarchical classification", "large-scale classification", "meta-learning", "rademacher complexity", "taxonomy adaptation"], "How to center deep Boltzmann machines": ["artificial neural network", "auto encoder", "centering", "contrastive divergence", "deep boltzmann machine", "enhanced gradient", "generative model", "natural gradient", "parallel tempering", "restricted boltzmann machine", "stochastic maximum likelihood"], "Control function instrumental variable estimation of nonlinear causal effect models": ["causal inference", "control function estimator", "endogenous variable", "instrumental variable method", "pretest estimator", "two stage least squares estimator"], "Structure learning in Bayesian networks of a moderate size by efficient sampling": ["bayesian model averaging", "bayesian networks", "dag sampling", "dynamic programming", "order sampling", "structure learning"], "Spectral methods meet EM: a provably optimal algorithm for crowdsourcing": ["crowdsourcing", "dawid-skene model", "em", "minimax rate", "non-convex optimization", "spectral methods"], "Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models": ["expectation propagation", "gaussian latent variable model", "laplace approximation", "leave-one-out cross-validation", "predictive performance"], "\u03b5-PAL: an active learning approach to the multi-objective optimization problem": ["active learning", "bayesian optimization", "design space exploration", "multi-objective optimization", "pareto optimality"], "Trend filtering on graphs": ["fused lasso", "graph smoothing", "local adaptivity", "total variation denoising", "trend filtering"], "Multi-task learning for straggler avoiding predictive job scheduling": [], "Interleaved text/image deep mining on a large-scale radiology database for automated image interpretation": ["convolutional neural networks", "deep learning", "medical imaging", "natural language processing", "topic models"], "Distribution-matching embedding for visual domain adaptation": ["distribution matching", "domain adaptation", "domain invariant representations", "hellinger distance", "maximum mean discrepancy"], "Monotonic calibrated interpolated look-up tables": ["interpolation", "interpretability", "look-up tables", "monotonicity"], "Are random forests truly the best classifiers?": ["benchmarking", "classification", "neural networks", "random forests", "support vector machines"], "Minimax adaptive estimation of nonparametric hidden Markov models": ["hidden markov models", "minimax adaptive estimation", "nonparametric estimation", "oracle inequality", "penalized least-squares"], "Decrypting \"cryptogenic\" epilepsy: semi-supervised hierarchical conditional random fields for detecting cortical lesions in MRI-negative patients": ["conditional random fields", "epilepsy", "focal cortical dysplasia", "lof"], "Fused lasso approach in regression coefficients clustering: learning parameter heterogeneity in data integration": ["data integration", "extended bic", "fused lasso", "generalized linear models"], "The LRP toolbox for artificial neural networks": ["artificial neural networks", "computer vision", "deep learning", "explaining classifiers", "layer-wise relevance propagation"], "Equivalence of graphical lasso and thresholding for sparse graphs": ["brain connectivity networks", "electrical circuits", "graphical lasso", "graphical models", "sparse graphs"], "A network that learns strassen multiplication": ["strassen multiplication", "sum-product networks", "tensor decomposition"], "Revisiting the Nystr\u00f6m method for improved large-scale machine learning": ["kernel methods", "low-rank approximation", "numerical linear algebra", "nystr\u00f6m approximation", "randomized algorithms"], "Improving structure MCMC for Bayesian networks through Markov blanket resampling": ["bayesian inference", "directed acyclic graph", "markov chain monte carlo", "probabilistic graphical models"], "Volumetric spanners: an efficient exploration basis for learning": ["barycentric spanner", "hard margin linear regression", "linear bandits", "volumetric spanner"], "Quasi-Monte Carlo feature maps for shift-invariant kernels": [], "Variational dependent multi-output Gaussian process dynamical systems": ["dynamical system", "gaussian process", "multi-output modeling", "variational inference"], "Multiple output regression with latent noise": ["bayesian reduced-rank regression", "latent signal-to-noise ratio", "latent variable models", "multiple-output regression", "nonparametric bayes", "shrinkage priors", "structured noise", "weak effects"], "The constrained dantzig selector with enhanced consistency": ["compressed sensing", "dantzig selector", "finite sample", "regularization methods", "sparse modeling", "ultra-high dimensionality"], "Bootstrap-based regularization for low-rank matrix estimation": ["correspondence analysis", "empirical bayes", "l\u00e9vy bootstrap", "singular-value decomposition"], "Bayesian optimization for likelihood-free inference of simulator-based statistical models": ["approximate bayesian computation", "bayesian inference", "computational efficiency", "intractable likelihood", "latent variables"], "On lower and upper bounds in smooth and strongly convex optimization": ["accelerated gradient descent", "full gradient descent", "heavy ball method", "smooth and strongly convex optimization"], "Dual control for approximate Bayesian reinforcement learning": ["bayesian inference", "control", "gaussian processes filtering", "reinforcement learning"], "Multiple-instance learning from distributions": ["classification", "learning theory", "multiple-instance learning", "ranking"], "An online convex optimization approach to Blackwell's approachability": ["approachability", "online convex optimization", "repeated games with vector payoffs"], "A well-conditioned and sparse estimation of covariance and inverse covariance matrices using a joint penalty": ["eigenvalue penalty", "penalized estimation", "sparsity"], "String and membrane Gaussian processes": ["gaussian processes", "nonstationary kernels", "point process priors", "reversible-jump mcmc", "scalable bayesian nonparametrics", "string gaussian processes"], "Extracting PICO sentences from clinical trial reports using supervised distant supervision": ["data extraction", "distant supervision", "evidence-based medicine", "natural language processing", "text mining"], "Cross-corpora unsupervised learning of trajectories in autism spectrum disorders": ["disease progression model", "dynamic topic model"], "Adjusting for chance clustering comparison measures": ["adjustment for chance", "clustering comparison", "clustering validation", "generalized information theoretic measures", "pair-counting measures"], "Refined error bounds for several learning algorithms": ["active learning", "minimax analysis", "pac learning", "sample complexity", "statistical learning theory"], "Synergy of monotonic rules": ["classification", "conditional probability", "ensemble learning", "intelligent teacher", "kernel functions", "knowledge transfer", "learning theory", "privileged information", "regression", "support vector machines", "svm+", "synergy"], "Pymanopt: a python toolbox for optimization on manifolds using automatic differentiation": ["manifold optimization", "non-convex optimization", "positive definite matrices", "projection matrices", "riemannian optimization", "rotation matrices", "symmetric matrices"], "CrossCat: a fully Bayesian nonparametric method for analyzing heterogeneous, high dimensional data": ["bayesian nonparametrics", "dirichlet processes", "markov chain monte carlo", "multivariate analysis", "semi-supervised learning", "structure learning", "unsupervised learning"], "Regularized policy iteration with nonparametric function spaces": ["approximate policy iteration", "finite-sample analysis", "nonparametric method", "regularization", "reinforcement learning"], "Multiscale adaptive representation of signals: I. the basic framework": ["adaframe", "dictionary learning", "wavelet frames/bi-frames"], "Sparse PCA via covariance thresholding": [], "Large scale visual recognition through adaptation using joint representation and multiple instance learning": ["computer vision", "deep learning", "large scale learning", "transfer learning"], "Covariance-based clustering in multivariate and functional data analysis": ["clustering", "covariance operator", "functional data analysis", "operator distance", "shrinkage estimation"], "MOCCA: mirrored convex/concave optimization for nonconvex composite functions": ["admm", "computed tomography", "mocca", "nonconvex", "penalized likelihood", "total variation"], "True online temporal-difference learning": ["eligibility traces", "forward-view equivalence", "temporal-difference learning"], "Penalized maximum likelihood estimation of multi-layered Gaussian graphical models": ["block coordinate descent", "consistency", "convergence", "graphical models", "penalized likelihood"], "Local network community detection with continuous optimization of conductance and weighted kernel K-means": ["community detection", "conductance", "k-means"], "Megaman: scalable manifold learning in python": ["dimension reduction", "graph embedding", "manifold learning", "python", "riemannian metric", "scalable methods"], "Kernel estimation and model combination in a bandit problem with covariates": ["contextual bandit problem", "exploration-exploitation tradeoff", "nonparametric regression", "regret bound", "upper confidence bound"], "A general framework for consistency of principal component analysis": ["high dimension low sample size", "pca", "random matrix", "spike model"], "Conditional independencies under the algorithmic independence of conditionals": ["causality", "faithfulness", "independence of conditionals", "kolmogorov complexity"], "Learning theory for distribution regression": ["kernel ridge regression", "mean embedding", "minimax optimality", "multi-instance learning", "two-stage sampled distribution regression"], "A differential equation for modeling Nesterov's accelerated gradient method: theory and insights": ["convex optimization", "differential equation", "first-order methods", "nesterov's accelerated scheme", "restarting"], "Importance weighting without importance weights: an efficient algorithm for combinatorial semi-bandits": ["bandit problems", "combinatorial optimization", "follow the perturbed leader", "importance weighting", "online learning", "semi-bandit feedback"], "New perspectives on k-support and cluster norms": ["convex optimization", "matrix completion", "multitask learning", "spectral regularization", "structured sparsity."], "Minimum density hyperplanes": ["clustering", "high-density clusters", "low-density separation", "projection pursuit", "semi-supervised classification"], "Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs": ["image analysis", "many classes", "nonlinear regression", "pattern recognition", "slow feature analysis"], "Universal approximation results for the temporal restricted Boltzmann machine and the recurrent temporal restricted Boltzmann machine": ["machine learning", "rtrbm", "trbm", "universal approximation"], "Exploration of the (non-)asymptotic bias and variance of stochastic gradient langevin dynamics": ["big data", "fixed step size", "langevin dynamics", "markov chain monte carlo"], "A general framework for constrained Bayesian optimization using information-based search": ["bayesian optimization", "constraints", "predictive entropy search"], "Optimal estimation and completion of matrices with biclustering structures": ["biclustering", "graphon", "matrix completion", "missing data", "sparse network", "stochastic block models"], "The teaching dimension of linear learners": ["karush-kuhn-tucker conditions", "optimization based learner", "vc-dimension"], "Augmentable gamma belief networks": ["bayesian nonparametrics", "deep learning", "multilayer representation", "poisson factor analysis", "topic modeling", "unsupervised learning"], "Optimal estimation of derivatives in nonparametric regression": ["linear combination", "nonparametric derivative estimation", "nonparametric regression", "optimal sequence", "taylor expansion"], "Double or nothing: multiplicative incentive mechanisms for crowdsourcing": ["crowdsourcing", "high-quality labels", "mechanism design", "proper scoring rules", "supervised learning"], "Joint structural estimation of multiple graphical models": ["consistency", "edge set recovery", "gaussian graphical model", "group lasso penalty", "structured sparsity"], "Support vector hazards machine: a counting process framework for learning risk scores for censored outcomes": ["biomarkers", "early disease detection", "neuroimaging", "risk bound", "risk prediction", "support vector machine", "survival analysis"], "Stable graphical models": ["bayesian networks", "differential expression", "gene expression", "linear regression", "stable distributions", "structure learning"], "Bounding the search space for global optimization of neural networks learning error: an interval analysis approach": ["algebraic solution", "bound constrained global optimization", "interval analysis", "interval linear equations", "neural network training"], "mlr: machine learning in R": ["benchmarking", "data mining", "feature selection", "hyperparameter tuning", "machine learning", "model selection", "r", "visualization"], "Feature-level domain adaptation": ["covariate shift", "domain adaptation", "risk minimization", "transfer learning"], "Semiparametric mean field variational Bayes: general principles and numerical issues": ["bayesian computing", "factor graph", "fixed-form variational bayes", "fixed-point iteration", "non-conjugate variational message passing", "nonlinear conjugate gradient method"], "Online PCA with optimal regret": ["expert setting", "gradient descent", "k-sets", "matrix exponentiated gradient algorithm", "online learning", "pca", "regret bounds"], "Efficient computation of Gaussian process regression for large spatial data sets by patching local Gaussian processes": ["boundary value problem", "constrained gaussian process regression", "kriging", "local regression", "spatial prediction", "variational problem"], "bandicoot: a python toolbox for mobile phone metadata": ["cdr", "feature engineering", "mobile phone metadata", "python", "visualization"], "Input output kernel regression: supervised and semi-supervised structured output prediction with operator-valued kernels": ["operator-valued kernel", "output kernel regression", "semi-supervised learning", "structured output prediction", "vector-valued rkhs"], "A note on the sample complexity of the Er-SpUD algorithm by Spielman, Wang and Wright for exact recovery of sparsely used dictionaries": ["er-spud algorithm", "exact recovery", "l1 minimization", "sample complexity", "sparse dictionaries"], "The asymptotic performance of linear echo state neural networks": ["echo state networks", "linear networks", "mean square error", "random matrix theory", "recurrent neural networks"], "On the consistency of the likelihood maximization vertex nomination scheme: bridging the gap between maximum likelihood estimation and graph matching": ["graph inference", "graph matching", "graph mining", "stochastic block model", "vertex nomination"], "Characteristic kernels and infinitely divisible distributions": ["characteristic kernel", "conjugate kernel", "convolution trick", "infinitely divisible distribution", "kernel mean"], "Consistency of cheeger and ratio graph cuts": ["balanced cut", "consistency", "data clustering", "graph partitioning"], "Jointly informative feature selection made tractable by Gaussian modeling": ["entropy", "feature selection", "mixture of gaussians", "mutual information"], "Learning with differential privacy: stability, learnability and the sufficiency and necessity of ERM principle": ["characterization", "differential privacy", "learnability", "privacy-preserving machine learning", "stability"], "fastFM: a library for factorization machines": ["context-aware recommendation", "matrix factorization", "mcmc", "python"], "The factorized self-controlled case series method: an approach for estimating the effects of many drugs on any outcomes": ["bayesian analysis", "drug safety", "effect size estimation", "matrix factorization", "self-controlled case series"], "Electronic health record analysis via deep Poisson factor models": ["deep learning", "electronic health records", "multi-modality learning", "phenotyping", "poisson factor model"], "Low-rank doubly stochastic matrix decomposition for cluster analysis": ["cluster analysis", "doubly stochastic matrix", "manifold", "multiplicative updates", "probabilistic relaxation"], "A new algorithm and theory for penalized regression-based clustering": ["alternating direction method of multipliers", "clustering consistency", "difference of convex programming", "truncated l1-penalty"], "Classification of imbalanced data with a geometric digraph family": ["class cover catch digraphs", "class cover problem", "class imbalance problem", "class overlapping problem", "graph domination", "prototype selection", "support estimation"], "A variational approach to path estimation and parameter inference of hidden diffusion processes": ["diffusion processes", "hidden markov model", "optimal control", "stochastic differential equations", "variational inference"], "One-class classification of point patterns of extremes": ["asymptotic theory", "class imbalance", "extreme value theory", "novelty detection", "sequence classification"], "On the influence of momentum acceleration on online learning": ["convergence rate", "heavy-ball method", "mean-square-error analysis", "momentum acceleration", "nesterov's method", "online learning", "stochastic gradient"], "Data-driven rank breaking for efficient rank aggregation": ["plackett-luce model", "rank aggregation", "sample complexity"], "Optimal learning rates for localized SVMs": ["least squares regression", "localization", "support vector machines"], "Bipartite ranking: a risk-theoretic perspective": ["bayes-optimality", "bipartite ranking", "class-probability estimation", "proper losses", "ranking the best"], "Bayesian group factor analysis with structured sparsity": ["bayesian structured sparsity", "canonical correlation analysis", "mixture models", "parameter expansion", "sparse and low-rank matrix decomposition", "sparse priors"], "Machine learning in an auction environment": ["auctions", "explore/exploit", "machine learning", "online advertising"], "Wavelet decompositions of Random Forests: smoothness analysis, sparse approximation and applications": ["adaptive approximation", "besov spaces", "feature importance", "random forest", "wavelets"], "Mutual information based matching for causal inference with observational data": ["matching", "mutual information", "observational causal inference", "optimization", "subset selection"], "Online trans-dimensional von Mises-Fisher mixture models for user profiles": ["bayesian nonparametric", "mixture models", "temporal evolution", "user modelling", "von mises-fisher"], "Multivariate spearman's \u03c1 for aggregating ranks using copulas": [], "Nonparametric network models for link prediction": ["bayesian nonparametrics", "dirichlet process", "gibbs sampling", "hierarchical modeling", "networks"], "Guarding against spurious discoveries in high dimensions": ["bootstrap", "gaussian approximation", "generalized linear models", "l1 regression", "model selection", "sparsity", "spurious correlation", "spurious fit"], "Bayesian graphical models for multivariate functional data": ["functional data analysis", "gaussian process", "graphical model", "model uncertainty", "stochastic search"], "Neural autoregressive distribution estimation": ["deep learning", "density modeling", "neural networks", "unsupervised learning"], "ERRATA: on the estimation of the gradient lines of a density and the consistency of the mean-shift algorithm": [], "Modelling interactions in high-dimensional data with backtracking": ["high-dimensional data", "interactions", "lasso", "path algorithm"], "Choice of V for V-fold cross-validation in least-squares density estimation": ["density estimation", "leave-one-out", "leave-p-out", "model selection", "monte-carlo cross-validation", "penalization", "resampling penalties", "v-fold cross-validation"], "Towards more efficient SPSD matrix approximation and CUR matrix decomposition": ["cur matrix decomposition", "kernel approximation", "matrix factorization", "the nystr\u00f6m method"], "Multi-objective Markov decision processes for data-driven decision support": ["clinical decision support", "evidence-based medicine", "markov decision processes", "multi-objective optimization", "reinforcement learning"], "Measuring dependence powerfully and equitably": ["equitability", "maximal information coefficient", "mutual information", "statistical power", "total information coefficient"], "Neyman-Pearson classification under high-dimensional settings": ["classification", "high-dimension", "naive bayes", "neyman-pearson paradigm", "np oracle inequality", "plug-in approach", "screening"], "A statistical perspective on randomized sketching for ordinary least-squares": ["algorithmic leveraging", "random projection", "randomized linear algebra", "sketching", "statistical efficiency", "statistical leverage"], "Learning planar ising models": ["graphical models", "ising models"], "Newton-stein method: an optimization method for GLMs via Stein's lemma": ["generalized linear models", "newton's method", "optimization", "sub-sampling"], "Bayesian decision process for cost-efficient dynamic ranking via crowdsourcing": ["bayesian", "crowdsourced ranking", "dynamic programming", "knowledge gradient", "markov decision process", "moment matching"], "Multi-scale classification using localized spatial depth": ["bayes classifier", "elliptic distributions", "generalized additive models", "hdlss asymptotics", "uniform strong consistency", "weighted aggregation of posteriors"], "On Bayes risk lower bounds": ["bayes risk", "f-divergence", "f-informativity", "fano's inequality", "minimax risk", "smoothed analysis"], "Weak convergence properties of constrained emphatic temporal-difference learning with constant and slowly diminishing stepsize": ["approximate policy evaluation", "convergence", "importance sampling", "markov decision processes", "reinforcement learning", "stochastic approximation", "temporal-difference methods"], "RLScore: regularized least-squares learners": ["cross-validation", "feature selection", "kernel methods", "kronecker product kernel", "pair-input learning", "python", "regularized least-squares"], "Stability and generalization in structured prediction": ["generalization bounds", "learning theory", "pac-bayes", "structured prediction"], "Composite multiclass losses": ["admissibility", "classification calibration", "convexity and quasi-convexity of losses", "link functions", "margin losses", "minimaxity", "mixability", "multiclass losses", "parametrisations and representations of loss functions", "proper losses", "superprediction set"], "Learning latent variable models by pairwise cluster comparison Part I: theory and overview": ["causal discovery", "clustering", "learning latent variable model", "multiple indicator model", "pure measurement model"], "GenSVM: a generalized multiclass support vector machine": ["classifier comparison", "iterative majorization", "mm algorithm", "multiclass classification", "support vector machines", "svm"], "Scalable approximate Bayesian inference for outlier detection under informative sampling": ["bayesian hierarchical models", "clustering", "hierarchical dirichlet process", "optimization", "survey sampling"], "Approximate Newton methods for policy search in Markov decision processes": ["function approximation", "markov decision processes", "newton method", "reinforcement learning"], "Gains and losses are fundamentally different in regret minimization: the sparse case": ["bandit", "regret minimization", "sparsity"], "Linear convergence of randomized feasible descent methods under the weak strong convexity assumption": ["convergence theory", "feasible descent method", "iteration complexity", "stochastic methods", "weak strong convexity"], "A practical scheme and fast algorithm to tune the lasso with optimality guarantees": ["high-dimensional regression", "lasso", "oracle inequalities", "regularization parameter", "tuning parameter"], "A characterization of linkage-based hierarchical clustering": [], "Learning latent variable models by pairwise cluster comparison Part II: algorithm and evaluation": ["clustering", "graphical models", "learning latent variable models", "pure measurement model"], "Integrative analysis using coupled latent variable models for individualizing prognoses": ["conditional random fields", "disease trajectories", "gaussian processes", "latent variable models", "precision medicine", "prediction of functional targets"], "An error bound for L1-norm support vector machine coefficients in ultra-high dimension": ["error bound", "feature selection", "l1-norm svm", "non-convex penalty", "oracle property", "support vector machine", "ulta-high dimension"], "Blending learning and inference in conditional random fields": [], "Distributed submodular maximization": ["approximation algorithms", "distributed computing", "greedy algorithms", "map-reduce", "submodular functions"], "On the properties of variational approximations of Gibbs posteriors": []}, {"Emilie Kaufmann": "LTCI, CNRS, T\u00e9l\u00e9com ParisTech, Paris", "Olivier Capp\u00e9": "Institut de Math\u00e9matiques de Toulouse, Universit\u00e9 de Toulouse, Toulouse Cedex 9", "Mauro Maggioni": "Department of Mathematics, University of Southern California, Los Angeles, CA", "Stanislav Minsker": "Department of Mathematics and Statistics, Georgetown University, Washington D.C.", "Azadeh Khaleghi": "INRIA Lille, Villeneuve d'Ascq, France", "Daniil Ryabko": "Universit\u00e9 de Lille, CRIStAL, UMR, CNRS, Villeneuve d'Ascq, France", "J\u00e9r\u00e9mie Mary": "Universit\u00e9 de Lille, CRIStAL, UMR, CNRS, Villeneuve d'Ascq, France", "Rico Blaser": "Department of Statistics, London School of Economics, London, UK", "Alessio Benavoli": "Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Scuola Universitaria Professionale della Svizzera italiana, Universit\u00e0 della Svizzera italiana, Manno, Switzerland", "Giorgio Corani": "Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Scuola Universitaria Professionale della Svizzera italiana, Universit\u00e0 della Svizzera italiana, Manno, Switzerland", "Olivier Collier": "Laboratoire de Statistique, ENSAE, CREST, Malakoff, France", "Yee Whye Teh": "Department of Statistics and Applied Probability, National University of Singapore, Singapore", "Alexandre H. Thiery": "Department of Statistics, University of Oxford, Oxford, UK", "\u00c7a\u01e7lar G\u00fcl\u00e7ehre": "D\u00e9partement d'informatique et de recherche op\u00e9rationnelle, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada", "Konrad Rieck": "University of G\u00f6ttingen, G\u00f6ttingen, Germany", "Luke Bornn": "Informatics Institute, Amsterdam, Netherlands", "Alan Yuille": "Allen Institute for Artificial Intelligence, Seattle, WA", "Yu-Xiang Wang": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Aviv Tamar": "Yahoo! Research Labs, Haifa, Israel", "Dotan Di Castro": "Department of Electrical Engineering, The Technion - Israel Institute of Technology, Haifa, Israel", "Harish G. Ramaswamy": "Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India", "Joonseok Lee": "Google Research, Mountain View, CA", "Seungyeon Kim": "LinkedIn, Mountain View, CA", "Yoram Singer": "Google Research, Mountain View, CA", "Xiang Zhang": "Department of Statistics, North Carolina State University, Raleigh, NC", "Yichao Wu": "Department of Statistics, The University of Minnesota, Minneapolis, MN", "Lan Wang": "Department of Statistics, North Carolina State University, Raleigh, NC", "Peter Kairouz": "Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL", "Sewoong Oh": "Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL", "Daniel Hsu": "Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel", "Alessandro Lazaric": "Adobe Research and INRIA Lille, France", "Mohammad Ghavamzadeh": "Rafael Advanced Defence System, Israel", "Hachem Kadri": "Ecole Centrale de Lille, CRIStAL, UMR, CNRS, Villeneuve d'Ascq, France", "Emmanuel Duflos": "Universit\u00e9 de Lille, CRIStAL, UMR, CNRS, Villeneuve d'Ascq, France", "Philippe Preux": "INSA de Rouen, LITIS, St Etienne du Rouvray, France", "St\u00e9phane Canu": "Universit\u00e9 de Rouen, LITIS, St Etienne du Rouvray, France", "Alain Rakotomamonjy": "ENS Cachan, CMLA, UMR, CNRS, Cachan Cedex, France", "Jesse Read": "Department of Computer Science, University of Waikato, New Zealand", "Peter Reutemann": "Department of Computer Science, University of Waikato, New Zealand", "Bernhard Pfahringer": "Department of Computer Science, University of Waikato, New Zealand", "Samory Kpotufe": "Rutgers University, New Brunswick, NJ", "Abdeslam Boularias": "University of Bonn, Germany", "Thomas Schultz": "Seoul National University of Science & Technology (SeoulTech), Korea", "Dmitry Adamskiy": "Centrum Wiskunde & Informatica, Amsterdam, The Netherlands", "Wouter M. Koolen": "School of Computing, Engineering and Mathematics, University of Brighton, Brighton, UK", "Alexey Chernov": "Computer Learning Research Centre and Department of Computer Science, University of London, Egham, Surrey, UK", "Michael L. Valenzuela": "Electrical and Computer Engineering Department, University of Arizona, Tucson, AZ", "H\u00e0 Quang Minh": "Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy", "Loris Bazzani": "Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy", "Lucas Mentch": "Department of Statistical Science, Cornell University, Ithaca, NY", "Yudong Chen": "Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA", "Daniel Hern\u00e1ndez-Lobato": "Quantitative Risk Research, Madrid, Spain", "David Lopez-Paz": "Universidad Aut\u00f3noma de Madrid, Madrid, Spain", "Ruth Heller": "Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel", "Shachar Kaufman": "Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel", "Robert J. B. Goudie": "German Centre for Neurodegenerative Diseases, Bonn, Germany", "Fran\u00e7ois Denis": "Aix Marseille Universit\u00e9, CNRS, LIF, UMR, Marseille Cedex 9, France", "Mattias Gybels": "Universit\u00e9 de Lyon, UJM-Saint-Etienne, CNRS, UMR, Saint-Etienne, France", "Joris M. Mooij": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Jonas Peters": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Dominik Janzing": "Institute for Atmospheric and Climate Science, ETH Z\u00fcrich, Z\u00fcrich, Switzerland", "Jakob Zscheischler": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Andr\u00e9 R. Gon\u00e7alves": "School of Electrical and Computer Engineering, University of Campinas, S\u00e3o Paulo, Brazil", "Fernando J. Von Zuben": "Computer Science Department, University of Minnesota, Twin Cities, Minneapolis", "Evan Sparks": "UC Berkeley, Berkeley, CA", "Shivaram Venkataraman": "Databricks, San Francisco, CA", "Jeremy Freeman": "Netfix, Los Gatos, CA", "Doris Xin": "Databricks, San Francisco, CA", "Michael J. Franklin": "Stanford and Databricks, Stanford, CA", "Reza Zadeh": "MIT and Databricks, San Francisco, CA", "Bin Li": "School of Information Systems, Singapore Management University, Singapore", "Julianus Pfeuffer": "Freie Universit\u00e4t Berlin, Department of Informatik, Berlin, Germany and The Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany", "Shiliang Zhang": "Department of Electrical Engineering and Computer Science, York University,Toronto, Ontario,Canada", "Hui Jiang": "National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, Anhui, China", "Sergey Levine": "Division of Computer Science, University of California, Berkeley, CA", "Chelsea Finn": "Division of Computer Science, University of California, Berkeley, CA", "Trevor Darrell": "Department of Computer Science, University of Massachusetts, Lowell, Massachusetts", "Chong Zhang": "Department of Statistics and Operations Research and Department of Genetics and Department of Biostatistics, Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, C ...", "Yufeng Liu": "Department of Statistics, North Carolina State University, Raleigh, NC", "Andreas C. Damianou": "Department of Informatics, Athens University of Economics and Business, Greece", "Michalis K. Titsias": "Dept. of Computer Science and Sheffield Institute for Translational Neuroscience, University of Sheffield, UK", "Ery Arias-Castro": "Department of Applied Economics and Statistics, University of Delaware, Newark, DE", "David Mason": "D\u00e9partement de Math\u00e9matiques, IRMAR, UMR, CNRS, Universit\u00e9 Rennes II, France", "Ana M. Mart\u00ednez": "Faculty of Information Technology, Monash University, VIC, Australia", "Geoffrey I. Webb": "College of Information Science, Faculty of Information Technology, Nanjing Audit University, Monash University, China, Australia", "Shenglei Chen": "Faculty of Information Technology, Monash University, VIC, Australia", "\u00dcr\u00fcn Do\u01e7an": "Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Germany", "Tobias Glasmachers": "Department of Computer Science, University of Copenhagen, Denmark", "Sherief Abdallah": "Centrum Wiskunde & Informatica, Amsterdam, The Netherlands and Maastricht University, The Netherlands", "Jing Lu": "School of Information Systems, Singapore Management University, Singapore", "Steven C. H. Hoi": "Department of Computer Science, University of Chicago, Chicago IL", "Jialei Wang": "Institute for Infocomm Research, A*STAR, Connexis, Singapore", "Peilin Zhao": "State Key Lab of Management and Control for Complex System, Chinese Academy of Sciences, Beijing, China", "Krikamol Muandet": "Department of Statistics, Pennsylvania State University, University Park, PA", "Bharath Sriperumbudur": "The Institute of Statistical Mathematics, Tokyo, Japan", "Kenji Fukumizu": "Gatsby Computational Neuroscience Unit, CSML, University College London, London, United Kingdom", "Arthur Gretton": "Empirical Inference Department, Max Planck Institute for Intelligent Systems, Germany", "Shusen Wang": "School of Mathematical Sciences, Peking University, Beijing, China", "Luo Luo": "Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China", "Wei Chen": "Microsoft, Sunnyvale, CA", "Yajun Wang": "Cornell University, Ithaca, NY", "Yang Yuan": "Tsinghua University, Beijing, China", "Ziteng Wang": "Department of Computer Science, University of California, Berkeley, CA", "Chi Jin": "Computational Biology & Bioinformactics, Duke University, Durham, NC", "Kai Fan": "Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, Beijing, China", "Jiaqi Zhang": "School of Mathematical Sciences, Peking University, Beijing, China", "Junliang Huang": "School of Mathematical Sciences, Peking University, Beijing, China", "Yiqiao Zhong": "Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, Beijing, China", "Alon Gonen": "School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel", "Dan Rosenbaum": "Department of Electrical Engineering, Technion, Israel Institute of Technology, Haifa, Israel", "Yonina C. Eldar": "School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel", "Mert Pilanci": "Department of Electrical Engineering and Computer Science, Department of Statistics, University of California, Berkeley, CA", "Chris. J. Oates": "Department of Statistics, University of Warwick, Coventry, UK", "Jim Q. Smith": "German Center for Neurodegenerative Diseases, Bonn, Germany", "St\u00e9phane Ivanoff": "Laboratoire de Biom\u00e9trie et Biologie \u00c9volutive, UMR, CNRS, Villeurbanne, France", "Franck Picard": "CEREMADE, UMR, CNRS, Universit\u00e9 Paris Dauphine, Paris, France", "Ricardo Silva": "Department of Statistics, University of Oxford, Oxford, UK", "Teppo Niinim\u00e4ki": "Helsinki Institute for Information Technology, Department of Computer Science, Aalto, Finland", "Pekka Parviainen": "Helsinki Institute for Information Technology, Department of Computer Science, University of Helsinki, Finland", "Nihar B. Shah": "Machine Learning Department, Microsoft Research, Redmond", "Sivaraman Balakrishnan": "Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA", "Joseph Bradley": "Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA", "Abhay Parekh": "Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA", "Kannan Ramchandran": "Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA", "Yaroslav Ganin": "Skolkovo Institute of Science and Technology (Skoltech), Skolkovo, Moscow Region, Russia", "Evgeniya Ustinova": "D\u00e9partement d'informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, Qu\u00e9bec, Canada", "Hana Ajakan": "D\u00e9partement d'informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, Qu\u00e9bec, Canada", "Pascal Germain": "D\u00e9partement d'informatique, Universit\u00e9 de Sherbrooke, Qu\u00e9bec, Canada", "Hugo Larochelle": "D\u00e9partement d'informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, Qu\u00e9bec, Canada", "Fran\u00e7ois Laviolette": "D\u00e9partement d'informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, Qu\u00e9bec, Canada", "Mario Marchand": "Skolkovo Institute of Science and Technology, Skolkovo, Moscow Region, Russia", "Aryan Mokhtari": "Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA", "G\u00e9rard Biau": "INRIA Saclay, Ile-de-France, Palaiseau, France", "Kevin Bleakley": "IRMAR, ENS Rennes, Bruz, France", "Andreas Andresen": "Weierstrass-Institute and Humboldt University Berlin, MIPT Moscow, Berlin, Germany", "Yossi Adi": "Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel", "Jure \u017dbontar": "Courant Institute of Mathematical Sciences, New York, NY", "Andr\u00e9 M. S. Barreto": "School of Computer Science, McGill University, Montreal, Canada", "Doina Precup": "School of Computer Science, McGill University, Montreal, Canada", "Daniel Russo": "Departments of Management Science and Engineering and Electrical Engineering, Stanford University, Stanford, California", "Rajarshi Guhaniyogi": "Department of Statistical Science, Duke University, Durham, NC", "Matey Neykov": "Department of Statistics, Harvard University, Cambridge, MA", "Jun S. Liu": "Department of Biostatistics, Harvard University, Boston, MA", "P.-L. Giscard": "Department of Statistics, University of Oxford, Oxford, UK", "Z. Choo": "Department of Physics and Arnold Sommerfeld Center for Theoretical Physics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Munich, Germany", "S. J. Thwaite": "Department of Physics, University of Oxford, Clarendon Laboratory, Oxford, United Kingdom and Centre for Quantum Technologies, National University of Singapore, Singapore", "Sergio Escalera": "University of Texas", "Vassilis Athitsos": "ChaLearn, Berkeley, California", "Richard S. Sutton": "Reinforcement Learning and Artificial Intelligence Laboratory, Department of Computing Science, University of Alberta, Alberta, Canada", "A. Rupam Mahmood": "Reinforcement Learning and Artificial Intelligence Laboratory, University of Alberta, Edmonton, AB", "Mehryar Mohri": "Courant Institute of Mathematical Sciences, New York, NY", "Peter Richt\u00e1rik": "Industrial and Systems Engineering Department, Lehigh University, H.S. Mohler Laboratory, Bethlehem, PA", "Stephan Cl\u00e9men\u00e7on": "LTCI, CNRS, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay, Paris, France", "Igor Colin": "INRIA Lille-Nord Europe, France", "Junhong Lin": "DIBRIS, Universit\u00e1 di Genova, Genova, Italy and Laboratory for Computational and Statistical Learning, Istituto Italiano di Tecnologia and Massachusetts Institute of Technology, Cambridge, MA", "Lorenzo Rosasco": "Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong, China", "Qirong Ho": "Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ", "Junming Yin": "School of Computer Science, Carnegie Mellon University, Pittsburgh, PA", "Jenna Wiens": "Department of EECS, Massachusetts Institute of Technology, Cambridge, MA", "John Guttag": "Microsoft Research, Redmond, WA", "Xin Wang": "Department of Computer Science and Engineering, University of Connecticut, Storrs, CT", "Jinbo Bi": "Health Services Innovation Center, Siemens Healthcare, Malvern, PA", "Jiangwen Sun": "Worldwide Research and Development, Pfizer Inc., Groton, CT", "Lei Yang": "Center of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China", "Shaogao Lv": "Department of Mathematics, City University of Hong Kong, Kowloon Tong, Hong Kong", "Steven Diamond": "Departments of Computer Science and Electrical Engineering, Stanford University, Stanford, CA", "Ermo Wei": "Department of Computer Science, George Mason University, Fairfax, VA", "Jun Fan": "Department of Mathematics City, University of Hong Kong, Kowloon, Hong Kong, China", "Yirong Wu": "Department of Statistics, University of Wisconsin-Madison, Madison, WI", "Ming Yuan": "Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI", "David Page": "Department of Genome Sciences, University of Washington-Seattle, Seattle, WA", "Jie Liu": "Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI", "Irene M. Ong": "Marshfield Clinic Research Foundation, Marshfield, WI", "Peggy Peissig": "Department of Radiology, University of Wisconsin-Madison, Madison, WI", "Wei-Sheng Chin": "Department of Computer Science, National Taiwan University, Taipei, Taiwan", "Bo-Wen Yuan": "Department of Computer Science, National Taiwan University, Taipei, Taiwan", "Meng-Yuan Yang": "Department of Computer Science, National Taiwan University, Taipei, Taiwan", "Yong Zhuang": "Department of Computer Science, National Taiwan University, Taipei, Taiwan", "Yu-Chin Juan": "Department of Computer Science, National Taiwan University, Taipei, Taiwan", "Fajwel Fogel": "CNRS & DI, UMR, \u00c9cole Normale Sup\u00e9rieure, Paris, France", "Alexandre d'Aspremont": "Microsoft Research, Cambridge, UK", "Xin Guo": "Department of Statistics, University of Wisconsin-Madison, Madison", "Le Song": "Computer Science Department, Z\u00fcrich, Switzerland", "M. Pawan Kumar": "Center for Visual Computing, CentraleSupelec, Ch\u00e2tenay-Malabry, France", "Dan Yang": "Department of Statistics, University of Pennsylvania, Philadelphia, PA", "Zongming Ma": "Yale University", "Christian Daniel": "Technische Universit\u00e4t Darmstadt, Fachbereich Informatik, Fachgruppe Intelligente Autonome Systeme, Darmstadt, Germany", "Gerhard Neumann": "Technische Universit\u00e4t Darmstadt, Fachbereich Informatik, Fachgruppe Intelligente Autonome Systeme, Darmstadt, Germany", "Oliver Kroemer": "Technische Universit\u00e4t Darmstadt, Fachbereich Informatik, Fachgruppe Intelligente Autonome Systeme, Darmstadt, Germany and Max-Planck-Institut f\u00fcr Intelligente Systeme, T\u00fcbingen, Ge ...", "Ashley Petersen": "Department of Biostatistics, University of Washington, Seattle, WA", "Noah Simon": "Departments of Biostatistics and Statistics, University of Washington, Seattle, WA", "Oscar Reyes": "Department of Computer Science, University of Holgu\u00edn, Holgu\u00edn, Cuba", "Eduardo P\u00e9rez": "Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain", "Mar\u00eda Del Carmen Rodr\u00edguez-Hern\u00e1ndez": "Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia", "Habib M. Fardoun": "Department of Computer Science and Numerical Analysis, University of C\u00f3rdoba, C\u00f3rdoba, Spain and Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia", "Gundram Leifert": "University of Rostock, Institute of Mathematics, Rostock, Germany", "Tobias Strau\u00df": "University of Rostock, Institute of Mathematics, Rostock, Germany", "Tobias Gr\u00fcning": "University of Rostock, Institute of Mathematics, Rostock, Germany", "Welf Wustlich": "University of Rostock, Institute of Mathematics, Rostock, Germany", "Rohit Babbar": "Viseo Research Center, Grenoble, France", "Eric Gaussier": "LIG, Universit\u00e9 Grenoble Alpes, CNRS, Grenoble, France", "Massih-Reza Amini": "LIG, Universit\u00e9 Grenoble Alpes, CNRS, Grenoble, France", "Jan Melchior": "Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Bochum, Germany", "Asja Fischer": "Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Bochum, Germany", "Zijian Guo": "Department of Statistics, University of Pennsylvania, Philadelphia", "Ru He": "Department of Computer Science, Iowa State University, Ames, IA", "Jin Tian": "Department of Statistics, Iowa State University, Ames, IA", "Yuchen Zhang": "Stern School of Business, New York University, New York, NY", "Xi Chen": "Department of Statistics, University of California, Berkeley, CA", "Dengyong Zhou": "Department of Electrical Engineering and Computer Science and Department of Statistics, University of California, Berkeley, Berkeley, CA", "Aki Vehtari": "Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Tommi Mononen": "Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Ville Tolvanen": "Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Tuomas Sivula": "Technical University of Denmark, Lyngby, Denmark", "Marcela Zuluaga": "Department of Computer Science, ETH Zurich, Zurich, Switzerland", "Andreas Krause": "Department of Computer Science, ETH Zurich, Zurich, Switzerland", "James Sharpnack": "Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA and Marianas Labs, Pittsburgh, PA", "Alexander J. Smola": "Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA and Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Neeraja J. Yadwadkar": "Division of Computer Science, University of California, Berkeley, CA", "Bharath Hariharan": "Division of Computer Science, University of California, Berkeley, CA", "Joseph E. Gonzalez": "Division of Computer Science, University of California, Berkeley, CA", "Hoo-Chang Shin": "Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD", "Le Lu": "Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD", "Lauren Kim": "Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD", "Ari Seff": "Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD", "Jianhua Yao": "Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD", "Mahsa Baktashmotlagh": "Australian National University & NICTA, Canberra, Australia", "Mehrtash Harandi": "CVLab, EPFL, Lausanne, Switzerland", "Maya Gupta": "Google, Mountain View, CA", "Andrew Cotter": "Google, Mountain View, CA", "Jan Pfeifer": "Google, Mountain View, CA", "Konstantin Voevodski": "Google, Mountain View, CA", "Kevin Canini": "Google, Mountain View, CA", "Alexander Mangylov": "Google, Mountain View, CA", "Wojciech Moczydlowski": "Google, Mountain View, CA", "Michael Wainberg": "Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada", "Babak Alipanahi": "Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada and Deep Genomics, Toronto, ON, Canada", "Yohann De Castro": "Laboratoire de Math\u00e9matiques d'Orsay, Univ. Paris-Sud, CNRS, Universit\u00e9 Paris-Saclay, Orsay, France", "\u00c9lisabeth Gassiat": "Laboratoire de Math\u00e9matiques d'Orsay, Univ. Paris-Sud, CNRS, Universit\u00e9 Paris-Saclay, Orsay, France", "Bilal Ahmed": "Comprehensive Epilepsy Center, Department of Neurology, School Of Medicine, New York University, New York, NY", "Thomas Thesen": "Comprehensive Epilepsy Center, Department of Neurology, School Of Medicine, New York University, New York, NY", "Karen E. Blackmon": "Comprehensive Epilepsy Center, Department of Neurology, School Of Medicine, New York University, New York, NY", "Ruben Kuzniekcy": "Comprehensive Epilepsy Center, Department of Neurology, School Of Medicine, New York University, New York, NY", "Orrin Devinsky": "College of Computer and Information Science, Northeastern University, Boston, MA", "Lu Tang": "Department of Biostatistics, University of Michigan, Ann Arbor, MI", "Sebastian Lapuschkin": "Singapore University of Technology, ISTD, Singapore", "Gr\u00e9goire Montavon": "Berlin Institute of Technology, Machine Learning Group, Berlin, Germany and Korea University, Department of Brain and Cognitive Engineering, Seoul, Korea", "Klaus-Robert M\u00fcller": "Fraunhofer Heinrich Hertz Institute, Video Coding and Analytics, Berlin, Germany", "Alex Gittens": "International Computer Science Institute and Department of Statistics, University of California, Berkeley, Berkeley, CA", "Chengwei Su": "Thayer School of Engineering, Dartmouth College, Hanover, NH", "Elad Hazan": "Yahoo Haifa Labs, Matam, Haifa, Israel", "Haim Avron": "School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel", "Vikas Sindhwani": "Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA", "Jiyan Yang": "International Computer Science Institute and Department of Statistics, University of California at Berkeley, Berkeley, CA", "Jing Zhao": "Department of Computer Science and Technology, East China Normal University, Shanghai, P. R. China", "Jussi Gillberg": "Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Pekka Marttinen": "Institute for Molecular Medicine Finland, University of Helsinki, Finland", "Matti Pirinen": "Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland", "Antti J. Kangas": "Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland", "Pasi Soininen": "Institute for Molecular Medicine Finland, University of Helsinki, Finland", "Mehreen Ali": "Department of Health, National Institute for Health and Welfare, Helsinki, Finland", "Aki S. Havulinna": "Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, UK", "Marjo-Riitta J\u00e4rvelin": "Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland", "Mika Ala-Korpela": "Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Yinfei Kong": "Department of Statistics and Finance, University of Science and Technololgy of China, Hefei, Anhui, China", "Zemin Zheng": "Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA", "Michael U. Gutmann": "Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki", "Yossi Arjevani": "School of Computer Science and Engineering, The Hebrew University, Givat Ram, Jerusalem, Israel", "Shai Shalev-Shwartz": "Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel", "Edgar D. Klenske": "Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany", "Gary Doran": "Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH", "Yves-Laurent Kom Samo": "Department of Engineering Science and Oxford-Man Institute, University of Oxford, Oxford, United Kingdom", "Byron C. Wallace": "Doctor Evidence, Santa Monica, CA", "Aakash Sharma": "Department of Chemistry, University of Texas at Austin, Austin, TX", "Mingxi Zhu": "Department of Primary Care & Public Health Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK", "Huseyin Melih Elibol": "Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Vincent Nguyen": "Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Scott Linderman": "Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Matthew Johnson": "Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Amna Hashmi": "Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Simone Romano": "Dept. of Computing and Information Systems, The University of Melbourne, VIC, Australia", "Nguyen Xuan Vinh": "Dept. of Computing and Information Systems, The University of Melbourne, VIC, Australia", "James Bailey": "Dept. of Computing and Information Systems, The University of Melbourne, VIC, Australia", "Vladimir Vapnik": "Applied Communication Sciences, Basking Ridge, NJ", "James Townsend": "RWTH Aachen University, Germany", "Niklas Koep": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Amir-massoud Farahmand": "Adobe Research, San Jose, CA", "Csaba Szepesv\u00e1ri": "Department of Electrical Engineering, The Technion, Haifa, Israel", "Cheng Tai": "School of Mathematical Sciences and BICMR, Peking University and Department of Mathematics and PACM, Princeton University, Princeton, NJ", "Yash Deshpande": "Departments of Electrical Engineering and Statistics, Stanford University, Stanford, CA", "Judy Hoffman": "Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA", "Deepak Pathak": "Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA", "Eric Tzeng": "Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA", "Jonathan Long": "Google Research and Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA", "Sergio Guadarrama": "Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA", "Francesca Ieva": "Modeling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milano, Italy", "Anna Maria Paganoni": "Modeling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milano, Italy", "Rina Foygel Barber": "Department of Radiology, University of Chicago, Chicago, IL", "Harm Van Seijen": "Reinforcement Learning and Artificial Intelligence Laboratory, University of Alberta, Edmonton, AB and Maluuba Research, Montreal, QC, Canada", "Patrick M. Pilarski": "Reinforcement Learning and Artificial Intelligence Laboratory, University of Alberta, Edmonton, AB", "Marlos C. Machado": "Reinforcement Learning and Artificial Intelligence Laboratory, University of Alberta, Edmonton, AB", "Jiahe Lin": "Department of Statistics, University of California, Berkeley, Berkeley, CA", "Sumanta Basu": "Department of Statistics, University of Michigan, Ann Arbor, MI", "Moulinath Banerjee": "Department of Statistics and Computer & Information Science & Engineering, University of Florida, Gainesville, FL", "Twan Van Laarhoven": "Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands", "James McQueen": "Department of Statistics, University of Washington, Seattle, WA", "Marina Meil\u0103": "e-Science Institute, University of Washington, Seattle, WA", "Jacob VanderPlas": "Department of Computer Science and Engineering, University of Washington, Seattle, WA", "Wei Qian": "School of Statistics, University of Minnesota, Minneapolis, MN", "Dan Shen": "School of Business, University of Hong Kong, Hong Kong", "Haipeng Shen": "Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC", "Zolt\u00e1n Szab\u00f3": "Department of Statistics, Pennsylvania State University, University Park, PA", "Bharath K. Sriperumbudur": "Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA", "Barnab\u00e1s P\u00f3czos": "University College London, London, UK", "Weijie Su": "Department of Electrical Engineering, Stanford University, Stanford, CA", "Stephen Boyd": "Departments of Statistics and Mathematics, Stanford University, Stanford, CA", "Gergely Neu": "Google Z\u00fcrich, Z\u00fcrich, Switzerland", "Andrew M. McDonald": "Istituto Italiano di Tecnologia, Genoa, Italy and Department of Computer Science, University College London, London, UK", "Massimiliano Pontil": "Department of Computer Science, University College London, London, UK", "Nicos G. Pavlidis": "Department of Mathematics and Statistics, Lancaster University, Lancaster, UK", "David P. Hofmeyr": "Department of Applied Mathematics, Liverpool John Moores University, Liverpool, UK", "Alberto N. Escalante-B.": "Theory of Neural Systems, Institut f\u00fcr Neuroinformatik, Ruhr-University Bochum, Bochum, Germany", "Simon Odense": "Department of Mathematics, University of Victoria, Victoria, BC, Canada", "Sebastian J. Vollmer": "School of Mathematics, University of Edinburgh, Edinburgh, UK", "Konstantinos C. Zygalakis": "Department of Statistics, University of Oxford, Oxford, UK", "Jos\u00e9 Miguel Hern\u00e1ndez-Lobato": "Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada", "Michael A. Gelbart": "School of Engineering and Applied Sciences, Harvard University, Cambridge, MA and Twitter, Cambridge, MA", "Ryan P. Adams": "Department of Engineering, Cambridge University, Cambridge, UK", "Matthew W. Hoffman": "Department of Engineering, Cambridge University, Cambridge, UK", "Chao Gao": "Yale University", "Yu Lu": "University of Pennsylvania", "Ji Liu": "Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI", "Mingyuan Zhou": "National Laboratory of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, Shaanxi, China", "Yulai Cong": "National Laboratory of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, Shaanxi, China", "Wenlin Dai": "Department of Mathematics, Hong Kong Baptist University, Hong Kong", "Tiejun Tong": "CEMSE Division, King Abdullah University of Science and Technology, Saudi Arabia", "Jing Ma": "Department of Statistics, University of Florida, Gainesville, FL", "Yuanjia Wang": "Biogen, Cambridge, MA", "Tianle Chen": "Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC", "Navodit Misra": "ISTI, CNR, Pisa, Italy and Max Planck Institute for Molecular Genetics, Berlin, Germany", "Stavros P. Adam": "Department of Computer Science and Information Systems, Birkbeck College, University of London, London, UK", "George D. Magoulas": "Department of Automation, Technological Educational Institute of Sterea Hellas, Evia, Greece", "Bernd Bischl": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Michel Lang": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Lars Kotthoff": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Julia Schiffner": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Jakob Richter": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Erich Studerus": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Giuseppe Casalicchio": "Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany", "Wouter M. Kouw": "Department of Intelligent Systems, Delft University of Technology, The Netherlands", "Laurens J. P. Van Der Maaten": "Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands and Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands", "Jesse H. Krijthe": "Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands and The Image Group, University of Copenhagen, Copenhagen, Denmark", "Jiazhong Nie": "Institute of Computing Science, Poznan University of Technology, Poland", "Wojciech Kot\u0142owski": "Department of Computer Science, University of California, Santa Cruz", "Chiwoo Park": "Department of Statistics, Texas A&M University, College Station, TX", "Yves-Alexandre De Montjoye": "Universit\u00e9 catholique de Louvain, ICTEAM, Louvain-la-Neuve, Belgium", "Luc Rocher": "MIT Media Lab., Cambridge, Cambridge MA", "C\u00e9line Brouard": "ENSIIE & LaMME, Universit\u00e9 d'\u00c9vry Val d'Essonne, CNRS, INRA, \u00c9vry Cedex, France and IBISC, Universit\u00e9 d'\u00c9vry Val d'Essonne, \u00c9vry Cedex, France", "Marie Szafranski": "LTCI, CNRS, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay, Paris, France and IBISC, Universit\u00e9 d'\u00c9vry Val d'Essonne, \u00c9vry Cedex, France", "Romain Couillet": "D\u00e9partement Informatique, Ecole Normale Sup\u00e9rieure, Paris, France", "Gilles Wainrib": "Laboratoire de Physique, Ecole Normale Sup\u00e9rieure de Lyon, Lyon, France", "Harry Sevi": "CentraleSup\u00e9lec, LSS, Universit\u00e9 ParisSud, Gif-sur-Yvette, France", "Vince Lyzinski": "Department of Computer Science, Johns Hopkins University, Baltimore, MD", "Keith Levin": "Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD", "Donniell E. Fishkind": "Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD", "Yu Nishiyama": "The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan", "Nicol\u00e1s Garc\u00eda Trillos": "Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA", "Dejan Slep\u010dev": "Department of Mathematics and Statistics, California State University, Long Beach, Long Beach, CA", "James Von Brecht": "Department of Mathematics, Loyola Marymount University, Los Angeles, CA", "Thomas Laurent": "Institute of Electrical Engineering, Swiss Federal Institute of Technology, Lausanne, Switzerland", "Jing Lei": "Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA and Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Ramin Moghaddass": "Department of Computer Science and Department of Electrical and Computer Engineering, Duke University, Durham, NC", "Cynthia Rudin": "Department of Statistics, Columbia University, New York, NY", "Ricardo Henao": "Duke School of Medicine, Duke Electrical and Computer Engineering, Duke University, Durham, NC", "James T. Lu": "Duke Electrical and Computer Engineering, Duke University, Durham, NC", "Joseph E. Lucas": "Duke School of Medicine, Duke University, Durham, NC", "Jeffrey Ferranti": "Duke Electrical and Computer Engineering, Duke University, Durham, NC", "Zhirong Yang": "Department of Mathematics and Statistics, Helsinki Institute for Information Technology, University of Helsinki, Finland and Department of Biostatistics, University of Oslo, Norway", "Jukka Corander": "Department of Computer Science, Aalto University, Finland", "Chong Wu": "Department of Applied Statistics, Konkuk University, Seoul, South Korea and School of Statistics, University of Minnesota, Minneapolis, MN", "Sunghoon Kwon": "School of Statistics, University of Minnesota, Minneapolis, MN", "Xiaotong Shen": "Division of Biostatistics, University of Minnesota, Minneapolis, MN", "Art\u00fcr Manukyan": "Department of Statistics, University of Pittsburgh, Pittsburgh, PA", "Tobias Sutter": "Department of Mathematics, Louisiana State University", "Arnab Ganguly": "Department of Electrical Engineering and Information Technology, TU Darmstadt, Germany", "Stijn Luca": "University of Oxford, Department of Engineering Science, Oxford, UK", "David A. Clifton": "KU Leuven-Technology Campus Geel, Department of Electrical Engineering, Geel, Belgium", "Kun Yuan": "Department of Electrical Engineering, University of California, Los Angeles, CA", "Bicheng Ying": "Department of Electrical Engineering, University of California, Los Angeles, CA", "Ashish Khetan": "Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL", "Mona Meister": "Institute for Stochastics and Applications, University of Stuttgart, Stuttgart, Germany", "Aditya Krishna Menon": "Data61 and The Australian National University, Canberra, ACT, Australia", "Shiwen Zhao": "Department of Statistical Science, Duke University, Durham, NC", "Chuan Gao": "Departments of Statistical Science, Computer Science, Mathematics, Duke University, Durham, NC", "Sayan Mukherjee": "Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, NJ", "Patrick Hummel": "Microsoft Corp., Redmond, WA", "Oren Elisha": "School of Mathematical Sciences, University of Tel-Aviv and GE Global Research, Israel", "Lei Sun": "Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY and Department of Computer Science and Information Systems, University of Jyvaskyla, Jyvaskyla, Finland", "Xiangju Qin": "School of Computer Science, University College Dublin, Dublin 4, Ireland", "P\u00e1draig Cunningham": "Department of Statistics, University of Oxford, Oxford, UK", "Justin Bed\u00f6": "Data61, CSIRO, Canberra, ACT, Australia and Research School of Computer Science, The Australian National University, Australia and The Department of Electrical and Electronic Engineering, The Univ ...", "Jianqing Fan": "Department of Operations Research and Financial Engineering, Princeton Univeristy, Princeton, NJ", "Hongxiao Zhu": "Department of Mathematics and Statistics, Georgetown University, Washington, DC", "Nate Strawn": "Department of Statistical Science, Duke University, Durham, NC", "Marc-Alexandre C\u00f4t\u00e9": "Google DeepMind, London, UK", "Iain Murray": "Twitter, Cambridge, MA", "Sylvain Arlot": "CNRS, Univ. Nice Sophia Antipolis, LJAD, CNRS, UMR, Nice, France", "Zhihua Zhang": "Department of Statistics, Rutgers University, Piscataway, New Jersey", "Daniel J. Lizotte": "Department of Statistics, North Carolina State University, Raliegh, NC", "Yakir A. Reshef": "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA", "David N. Reshef": "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA", "Hilary K. Finucane": "Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA", "Pardis C. Sabeti": "School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Anqi Zhao": "Department of Statistics, Columbia University", "Yang Feng": "Department of Mathematics, Massachusetts Institute of Technology", "Lie Wang": "Department of Data Sciences and Operations, Marshall Business School, University of Southern California", "Garvesh Raskutti": "International Computer Science Institute and Department of Statistics, University of California at Berkeley, Berkeley, CA", "Diane Oyen": "Los Alamos National Laboratory, Los Alamos, NM", "Michael Chertkov": "Microsoft Research, Cambridge, MA", "Kevin Jiao": "Tippie College of Business, University of Iowa, Iowa City, Iowa", "Subhajit Dutta": "Theoretical Statistics and Mathematics Unit, Indian Statistical Institute, Kolkata, India", "Soham Sarkar": "Theoretical Statistics and Mathematics Unit, Indian Statistical Institute, Kolkata, India", "Adityanand Guntuboyina": "Computer Science Department, Stanford University, Stanford, CA", "Tapio Pahikkala": "Department of Information Technology, University of Turku, Finland", "Ben London": "Virginia Tech.", "Bert Huang": "University of California, Santa Cruz", "Robert C. Williamson": "Centre for Mathematical Sciences, University of Cambridge", "Elodie Vernet": "Australian National University and Data61", "Nuaman Asbeh": "Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel", "Gerrit J. J. Van Den Burg": "Econometric Institute Erasmus, University Rotterdam, Rotterdam, The Netherlands", "Thomas Furmston": "Department of Computer Science, University College London, London", "Guy Lever": "Department of Computer Science, University College London, London", "Joon Kwon": "Centre de Recherche en \u00c9conomie et Statistique, \u00c9cole Nationale de la Statistique et de l'administration \u00c9conomique, Malakoff, France", "Chenxin Ma": "Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand", "Rachael Tappenden": "Industrial and Systems Engineering, Lehigh University, Bethlehem, PA", "Micha\u00ebl Chichignoud": "Department of Statistics at University of Washington, Seattle, WA and Department of Biostatistics, University of Washington", "Johannes Lederer": "Department of Statistics and Department of Electrical Engineering and Computer Sciences, University of California at Berkeley", "Margareta Ackerman": "D.R.C. School of Computer Science, University of Waterloo, Waterloo, ON", "Peter Schulam": "Department of Computer Science, Johns Hopkins University, Baltimore, MD", "Bo Peng": "School of Statistics, University of Minnesota, Minneapolis, MN", "Tamir Hazan": "Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL", "Alexander G. 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Nice Sophia Antipolis, LJAD, CNRS, UMR, Nice, France": "France", "Department of Statistics, Rutgers University, Piscataway, New Jersey": "New Jersey", "Department of Statistics, North Carolina State University, Raliegh, NC": "America", "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA": "America", "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA": "America", "Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA": "America", "School of Engineering and Applied Sciences, Harvard University, Cambridge, MA": "America", "Department of Statistics, Columbia University": "Columbia University", "Department of Mathematics, Massachusetts Institute of Technology": "Massachusetts Institute of Technology", "Department of Data Sciences and Operations, Marshall Business School, University of Southern California": "University of Southern California", "Los Alamos National Laboratory, Los Alamos, NM": "America", "Microsoft Research, Cambridge, MA": "America", "Stern School of Business, New York University, New York": "New York", "Tippie College of Business, University of Iowa, Iowa City, Iowa": "Iowa", "Theoretical Statistics and Mathematics Unit, Indian Statistical Institute, Kolkata, India": "India", "Department of Statistics, University of California, Berkeley, CA": "America", "Computer Science Department, Stanford University, Stanford, CA": "America", "Department of Information Technology, University of Turku, Finland": "Finland", "Virginia Tech.": "irginia Tech.", "University of California, Santa Cruz": "Santa Cruz", "Centre for Mathematical Sciences, University of Cambridge": "University of Cambridge", "Australian National University and Data61": "ustralian National University and Data61", "Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel": "Israel", "Econometric Institute Erasmus, University Rotterdam, Rotterdam, The Netherlands": "The Netherlands", "Department of Computer Science, University College London, London": "London", "Centre de Recherche en \u00c9conomie et Statistique, \u00c9cole Nationale de la Statistique et de l'administration \u00c9conomique, Malakoff, France": "France", "Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand": "New Zealand", "Industrial and Systems Engineering, Lehigh University, Bethlehem, PA": "America", "Department of Statistics at University of Washington, Seattle, WA and Department of Biostatistics, University of Washington": "University of Washington", "Department of Statistics and Department of Electrical Engineering and Computer Sciences, University of California at Berkeley": "University of California at Berkeley", "D.R.C. School of Computer Science, University of Waterloo, Waterloo, ON": "America", "Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL": "America", "University of Toronto, Toronto, ON": "America", "School of Engineering and Applied Science, Yale University, New Haven": "New Haven", "Department of Informatics, University of Edinburgh, Edinburgh, United Kingdom": "United Kingdom", "ENSAE, Malakoff, France": "France"}]