jmlr_2014.json

[{"Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models": ["J\u00fcri Lember", "Alexey A. Koloydenko"], "Fast SVM training using approximate extreme points": ["Manu Nandan", "Pramod P. Khargonekar", "Sachin S. Talathi"], "Detecting click fraud in online advertising: a data mining approach": ["Richard Oentaryo", "Ee-Peng Lim", "Michael Finegold", "David Lo", "Feida Zhu", "Clifton Phua", "Eng-Yeow Cheu", "Ghim-Eng Yap", "Kelvin Sim", "Minh Nhut Nguyen", "Kasun Perera", "Bijay Neupane", "Mustafa Faisal", "Zeyar Aung", "Wei Lee Woon", "Wei Chen", "Dhaval Patel", "Daniel Berrar"], "EnsembleSVM: a library for ensemble learning using support vector machines": ["Marc Claesen", "Frank De Smet", "Johan A. K. Suykens", "Bart De Moor"], "A junction tree framework for undirected graphical model selection": ["Divyanshu Vats", "Robert D. Nowak"], "Axioms for graph clustering quality functions": ["Twan Van Laarhoven", "Elena Marchiori"], "Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso": ["Aleksandr Aravkin", "James V. Burke", "Alessandro Chiuso", "Gianluigi Pillonetto"], "Using trajectory data to improve bayesian optimization for reinforcement learning": ["Aaron Wilson", "Alan Fern", "Prasad Tadepalli"], "Information theoretical estimators toolbox": ["Zolt\u00e1n Szab\u00f3"], "Off-policy learning with eligibility traces: a survey": ["Matthieu Geist", "Bruno Scherrer"], "Early stopping and non-parametric regression: an optimal data-dependent stopping rule": ["Garvesh Raskutti", "Martin J. Wainwright", "Bin Yu"], "Unbiased generative semi-supervised learning": ["Patrick Fox-Roberts", "Edward Rosten"], "Node-based learning of multiple Gaussian graphical models": ["Karthik Mohan", "Palma London", "Maryam Fazel", "Daniela Witten", "Su-In Lee"], "The fastclime package for linear programming and large-scale precision matrix estimation in R": ["Haotian Pang", "Han Liu", "Robert Vanderbei"], "LIBOL: a library for online learning algorithms": ["Steven C. H. Hoi", "Jialei Wang", "Peilin Zhao"], "Improving Markov network structure learning using decision trees": ["Daniel Lowd", "Jesse Davis"], "Ground metric learning": ["Marco Cuturi", "David Avis"], "Link prediction in graphs with autoregressive features": ["Emile Richard", "St\u00e9phane Ga\u00efffas", "Nicolas Vayatis"], "Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression": ["Francis Bach"], "Random intersection trees": ["Rajen Dinesh Shah", "Nicolai Meinshausen"], "Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers": ["Brett L. Moore", "Larry D. Pyeatt", "Vivekanand Kulkarni", "Periklis Panousis", "Kevin Padrez", "Anthony G. Doufas"], "Clustering hidden Markov models with variational HEM": ["Emanuele Coviello", "Antoni B. Chan", "Gert R. G. Lanckriet"], "A novel M-estimator for robust PCA": ["Teng Zhang", "Gilad Lerman"], "Policy evaluation with temporal differences: a survey and comparison": ["Christoph Dann", "Gerhard Neumann", "Jan Peters"], "Active learning using smooth relative regret approximations with applications": ["Nir Ailon", "Ron Begleiter", "Esther Ezra"], "An extension of slow feature analysis for nonlinear blind source separation": ["Henning Sprekeler", "Tiziano Zito", "Laurenz Wiskott"], "Natural evolution strategies": ["Daan Wierstra", "Tom Schaul", "Tobias Glasmachers", "Yi Sun", "Jan Peters", "J\u00fcrgen Schmidhuber"], "Conditional random field with high-order dependencies for sequence labeling and segmentation": ["Nguyen Viet Cuong", "Nan Ye", "Wee Sun Lee", "Hai Leong Chieu"], "Ellipsoidal rounding for nonnegative matrix factorization under noisy separability": ["Tomohiko Mizutani"], "Improving prediction from dirichlet process mixtures via enrichment": ["Sara Wade", "David B. Dunson", "Sonia Petrone", "Lorenzo Trippa"], "Gibbs max-margin topic models with data augmentation": ["Jun Zhu", "Ning Chen", "Hugh Perkins", "Bo Zhang"], "A reliable effective terascale linear learning system": ["Alekh Agarwal", "Olivier Chapelle", "Miroslav Dud\u00edk", "John Langford"], "New learning methods for supervised and unsupervised preference aggregation": ["Maksims N. Volkovs", "Richard S. Zemel"], "Prediction and clustering in signed networks: a local to global perspective": ["Kai-Yang Chiang", "Cho-Jui Hsieh", "Nagarajan Natarajan", "Inderjit S. Dhillon", "Ambuj Tewari"], "Bayesian nonparametric comorbidity analysis of psychiatric disorders": ["Francisco J. R. Ruiz", "Isabel Valera", "Carlos Blanco", "Fernando Perez-Cruz"], "Robust near-separable nonnegative matrix factorization using linear optimization": ["Nicolas Gillis", "Robert Luce"], "Follow the leader if you can, hedge if you must": ["Steven De Rooij", "Tim Van Erven", "Peter D. Gr\u00fcnwald", "Wouter M. Koolen"], "Structured prediction via output space search": ["Janardhan Rao Doppa", "Alan Fern", "Prasad Tadepalli"], "Fully simplified multivariate normal updates in non-conjugate variational message passing": ["Matt P. Wand"], "Towards ultrahigh dimensional feature selection for big data": ["Mingkui Tan", "Ivor W. Tsang", "Li Wang"], "Adaptive sampling for large scale boosting": ["Charles Dubout", "Fran\u00e7ois Fleuret"], "Manopt, a matlab toolbox for optimization on manifolds": ["Nicolas Boumal", "Bamdev Mishra", "P.-A. Absil", "Rodolphe Sepulchre"], "Training highly multiclass classifiers": ["Maya R. Gupta", "Samy Bengio", "Jason Weston"], "Locally adaptive factor processes for multivariate time series": ["Daniele Durante", "Bruno Scarpa", "David B. Dunson"], "Iteration complexity of feasible descent methods for convex optimization": ["Po-Wei Wang", "Chih-Jen Lin"], "High-dimensional covariance decomposition into sparse Markov and independence models": ["Majid Janzamin", "Animashree Anandkumar"], "The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo": ["Matthew D. Homan", "Andrew Gelman"], "Confidence intervals for random forests: the jackknife and the infinitesimal jackknife": ["Stefan Wager", "Trevor Hastie", "Bradley Efron"], "Surrogate regret bounds for bipartite ranking via strongly proper losses": ["Shivani Agarwal"], "Adaptive minimax regression estimation over sparse lq-hulls": ["Zhan Wang", "Sandra Paterlini", "Fuchang Gao", "Yuhong Yang"], "Graph estimation from multi-attribute data": ["Mladen Kolar", "Han Liu", "Eric P. Xing"], "Hitting and commute times in large random neighborhood graphs": ["Ulrike Von Luxburg", "Agnes Radl", "Matthias Hein"], "Bayesian inference with posterior regularization and applications to infinite latent SVMs": ["Jun Zhu", "Ning Chen", "Eric P. Xing"], "Expectation propagation for neural networks with sparsity-promoting priors": ["Pasi Jyl\u00e4nki", "Aapo Nummenmaa", "Aki Vehtari"], "Pattern alternating maximization algorithm for missing data in high-dimensional problems": ["Nicolas St\u00e4dler", "Daniel J. Stekhoven", "Peter B\u00fchlmann"], "Dropout: a simple way to prevent neural networks from overfitting": ["Nitish Srivastava", "Geoffrey Hinton", "Alex Krizhevsky", "Ilya Sutskever", "Ruslan Salakhutdinov"], "Sparse factor analysis for learning and content analytics": ["Andrew S. Lan", "Andrew E. Waters", "Christoph Studer", "Richard G. Baraniuk"], "Causal discovery with continuous additive noise models": ["Jonas Peters", "Joris M. Mooij", "Dominik Janzing", "Bernhard Sch\u00f6lkopf"], "PyStruct: learning structured prediction in python": ["Andreas C. M\u00fcller", "Sven Behnke"], "The student-t mixture as a natural image patch prior with application to image compression": ["A\u00e4ron Van Den Oord", "Benjamin Schrauwen"], "Parallel MCMC with generalized elliptical slice sampling": ["Robert Nishihara", "Iain Murray", "Ryan P. Adams"], "Classifier cascades and trees for minimizing feature evaluation cost": ["Zhixiang Xu", "Matt J. Kusner", "Kilian Q. Weinberger", "Minmin Chen", "Olivier Chapelle"], "Particle gibbs with ancestor sampling": ["Fredrik Lindsten", "Michael I. Jordan", "Thomas B. Sch\u00f6n"], "Ramp loss linear programming support vector machine": ["Xiaolin Huang", "Lei Shi", "Johan A. K. Suykens"], "Clustering partially observed graphs via convex optimization": ["Yudong Chen", "Ali Jalali", "Sujay Sanghavi", "Huan Xu"], "A tensor approach to learning mixed membership community models": ["Animashree Anandkumar", "Rong Ge", "Daniel Hsu", "Sham M. Kakade"], "Cover tree Bayesian reinforcement learning": ["Nikolaos Tziortziotis", "Christos Dimitrakakis", "Konstantinos Blekas"], "Efficient state-space inference of periodic latent force models": ["Steven Reece", "Siddhartha Ghosh", "Alex Rogers", "Stephen Roberts", "Nicholas R. Jennings"], "Spectral learning of latent-variable PCFGs: algorithms and sample complexity": ["Shay B. Cohen", "Karl Stratos", "Michael Collins", "Dean P. Foster", "Lyle Ungar"], "On multilabel classification and ranking with bandit feedback": ["Claudio Gentile", "Francesco Orabona"], "Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization": ["Elad Hazan", "Satyen Kale"], "One-shot-learning gesture recognition using HOG-HOF features": ["Jakub Kone\u010dn\u00fd", "Michal Hagara"], "Contextual bandits with similarity information": ["Aleksandrs Slivkins"], "Boosting algorithms for detector cascade learning": ["Mohammad Saberian", "Nuno Vasconcelos"], "Efficient and accurate methods for updating generalized linear models with multiple feature additions": ["Amit Dhurandhar", "Marek Petrik"], "Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions": ["Shohei Shimizu", "Kenneth Bollen"], "A truncated EM approach for spike-and-slab sparse coding": ["Abdul-Saboor Sheikh", "Jacquelyn A. Shelton", "J\u00f6rg L\u00fccke"], "Efficient occlusive components analysis": ["Marc Henniges", "Richard E. Turner", "Maneesh Sahani", "Julian Eggert", "J\u00f6g L\u00fccke"], "Optimality of graphlet screening in high dimensional variable selection": ["Jiashun Jin", "Cun-Hui Zhang", "Qi Zhang"], "Tensor decompositions for learning latent variable models": ["Animashree Anandkumar", "Rong Ge", "Daniel Hsu", "Sham M. Kakade", "Matus Telgarsky"], "Bayesian entropy estimation for countable discrete distributions": ["Evan Archer", "Il Memming Park", "Jonathan W. Pillow"], "Confidence intervals and hypothesis testing for high-dimensional regression": ["Adel Javanmard", "Andrea Montanari"], "QUIC: quadratic approximation for sparse inverse covariance estimation": ["Cho-Jui Hsieh", "M\u00e1ty\u00e1s A. Sustik", "Inderjit S. Dhillon", "Pradeep Ravikumar"], "Multimodal learning with deep Boltzmann machines": ["Nitish Srivastava", "Ruslan Salakhutdinov"], "Optimal data collection for informative rankings expose well-connected graphs": ["Braxton Osting", "Christoph Brune", "Stanley J. Osher"], "Bayesian co-boosting for multi-modal gesture recognition": ["Jiaxiang Wu", "Jian Cheng"], "Effective string processing and matching for author disambiguation": ["Wei-Sheng Chin", "Yong Zhuang", "Yu-Chin Juan", "Felix Wu", "Hsiao-Yu Tung", "Tong Yu", "Jui-Pin Wang", "Cheng-Xia Chang", "Chun-Pai Yang", "Wei-Cheng Chang", "Kuan-Hao Huang", "Tzu-Ming Kuo", "Shan-Wei Lin", "Young-San Lin", "Yu-Chen Lu", "Yu-Chuan Su", "Cheng-Kuang Wei", "Tu-Chun Yin", "Chun-Liang Li", "Ting-Wei Lin", "Cheng-Hao Tsai", "Shou-De Lin", "Hsuan-Tien Lin", "Chih-Jen Lin"], "High-dimensional learning of linear causal networks via inverse covariance estimation": ["Po-Ling Loh", "Peter B\u00fchlmann"], "Recursive teaching dimension, VC-dimension and sample compression": ["Thorsten Doliwa", "Gaojian Fan", "Hans Ulrich Simon", "Sandra Zilles"], "Do we need hundreds of classifiers to solve real world classification problems?": ["Manuel Fern\u00e1ndez-Delgado", "Eva Cernadas", "Sen\u00e9n Barro", "Dinani Amorim"], "ooDACE toolbox: a flexible object-oriented Kriging implementation": ["Ivo Couckuyt", "Tom Dhaene", "Piet Demeester"], "Robust online gesture recognition with crowdsourced annotations": ["Long-Van Nguyen-Dinh", "Alberto Calatroni", "Gerhard Tr\u00f6ster"], "Accelerating t-SNE using tree-based algorithms": ["Laurens Van Der Maaten"], "Set-valued approachability and online learning with partial monitoring": ["Shie Mannor", "Vianney Perchet", "Gilles Stoltz"], "Learning graphical models with hubs": ["Kean Ming Tan", "Palma London", "Karthik Mohan", "Su-In Lee", "Maryam Fazel", "Daniela Witten"], "Inconsistency of Pitman-Yor process mixtures for the number of components": ["Jeffrey W. Miller", "Matthew T. Harrison"], "Active contextual policy search": ["Alexander Fabisch", "Jan Hendrik Metzen"], "Matrix completion with the trace norm: learning, bounding, and transducing": ["Ohad Shamir", "Shai Shalev-Shwartz"], "Statistical analysis of metric graph reconstruction": ["Fabrizio Lecci", "Alessandro Rinaldo", "Larry Wasserman"], "Alternating linearization for structured regularization problems": ["Xiaodong Lin", "Minh Pham", "Andrzej Ruszczy\u0144ski"], "The gesture recognition toolkit": ["Nicholas Gillian", "Joseph A. Paradiso"], "Convolutional nets and watershed cuts for real-time semantic Labeling of RGBD videos": ["Camille Couprie", "Cl\u00e9ment Farabet", "Laurent Najman", "Yann LeCun"], "On the bayes-optimality of F-measure maximizers": ["Willem Waegeman", "Krzysztof Dembczy\u0144ki", "Arkadiusz Jachnik", "Weiwei Cheng", "Eyke H\u00fcllermeier"], "SPMF: a Java open-source pattern mining library": ["Philippe Fournier-Viger", "Antonio Gomariz", "Ted Gueniche", "Azadeh Soltani", "Cheng-Wei Wu", "Vincent S. Tseng"], "Efficient learning and planning with compressed predictive states": ["William Hamilton", "Mahdi Milani Fard", "Joelle Pineau"], "Revisiting Stein's paradox: multi-task averaging": ["Sergey Feldman", "Maya R. Gupta", "Bela A. Frigyik"], "Multi-objective reinforcement learning using sets of pareto dominating policies": ["Kristof Van Moffaert", "Ann Now\u00e9"], "Seeded graph matching for correlated Erd\u00f6s-R\u00e9nyi graphs": ["Vince Lyzinski", "Donniell E. Fishkind", "Carey E. Priebe"], "Asymptotic accuracy of distribution-based estimation of latent variables": ["Keisuke Yamazaki"], "What regularized auto-encoders learn from the data-generating distribution": ["Guillaume Alain", "Yoshua Bengio"], "Revisiting Bayesian blind deconvolution": ["David Wipf", "Haichao Zhang"], "New results for random walk learning": ["Jeffrey C. Jackson", "Karl Wimmer"], "Transfer learning decision forests for gesture recognition": ["Norberto A. Goussies", "Sebasti\u00e1n Ubalde", "Marta Mejail"], "Semi-supervised eigenvectors for large-scale locally-biased learning": ["Toke J. Hansen", "Michael W. Mahoney"], "BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits": ["Ruben Martinez-Cantin"], "Order-independent constraint-based causal structure learning": ["Diego Colombo", "Marloes H. Maathuis"], "Effective sampling and learning for mallows models with pairwise-preference data": ["Tyler Lu", "Craig Boutilier"], "Robust hierarchical clustering": ["Maria-Florina Balcan", "Yingyu Liang", "Pramod Gupta"], "Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization": ["Thomas Desautels", "Andreas Krause", "Joel W. Burdick"], "Active lmitation learning: formal and practical reductions to I.I.D. learning": ["Kshitij Judah", "Alan P. Fern", "Thomas G. Dietterich", "Prasad adepalli"]}, {"Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models": ["admissible path", "decoder", "hmm", "hybrid", "interpolation", "map sequence", "minimum error", "optimal accuracy", "posterior decoding", "power transform", "risk", "segmental classification", "symbol-by-symbol", "viterbi algorithm"], "Fast SVM training using approximate extreme points": ["convex hulls", "extreme points", "large scale classification", "non-linear kernels", "support vector machines"], "Detecting click fraud in online advertising: a data mining approach": ["ensemble learning", "feature engineering", "fraud detection", "imbalanced classification"], "EnsembleSVM: a library for ensemble learning using support vector machines": ["bagging", "classification", "ensemble learning", "support vector machine"], "A junction tree framework for undirected graphical model selection": ["graph decomposition", "graphical model selection", "graphical models", "high-dimensional statistics", "junction trees", "markov random fields", "model selection"], "Axioms for graph clustering quality functions": ["axiomatic framework", "graph clustering", "modularity"], "Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso": ["bayesian regularization", "group lasso", "lasso", "marginal likelihood", "multiple kernel learning"], "Using trajectory data to improve bayesian optimization for reinforcement learning": ["bayesian", "markov decision process", "mdp", "optimization", "policy search", "reinforcement learning"], "Information theoretical estimators toolbox": ["association", "distribution kernel estimation", "divergence", "entropy", "gnu gplv3 (\u2265)", "independent subspace analysis and its extensions", "matlab/octave", "modularity", "multi-platform", "mutual information"], "Off-policy learning with eligibility traces: a survey": ["eligibility traces", "off-policy learning", "reinforcement learning", "value function estimation"], "Early stopping and non-parametric regression: an optimal data-dependent stopping rule": ["early stopping", "empirical processes", "kernel ridge regression", "non-parametric regression", "rademacher complexity", "reproducing kernel hilbert space", "stopping rule"], "Unbiased generative semi-supervised learning": ["asymptotic bounds", "bias", "generative model", "kullback-leibler", "semi-supervised"], "Node-based learning of multiple Gaussian graphical models": ["alternating direction method of multipliers", "gene regulatory network", "graphical model", "lasso", "multivariate normal", "structured sparsity"], "The fastclime package for linear programming and large-scale precision matrix estimation in R": ["high dimensional data", "linear programming", "parametric simplex method", "sparse precision matrix", "undirected graphical model"], "LIBOL: a library for online learning algorithms": ["big data analytics", "massive-scale classification", "online learning"], "Improving Markov network structure learning using decision trees": ["decision trees", "markov networks", "probabilistic methods", "structure learning"], "Ground metric learning": ["earth mover's distance", "metric learning", "metric nearness", "optimal transport distance"], "Link prediction in graphs with autoregressive features": ["autoregression", "graphs", "link prediction", "low-rank", "sparsity"], "Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression": ["logistic regression", "self-concordance", "stochastic approximation"], "Random intersection trees": ["high-dimensional classification", "interactions", "min-wise hashing", "sparse data"], "Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers": ["anesthesia", "bispectral index", "closed-loop control", "hypnosis", "propofol", "reinforcement learning"], "Clustering hidden Markov models with variational HEM": ["clustering", "hidden markov mixture model", "hidden markov model", "hierarchical em algorithm", "time-series classification", "variational approximation"], "A novel M-estimator for robust PCA": ["convex relaxation", "iteratively re-weighted least squares", "m-estimator", "principal components analysis", "robust statistics"], "Policy evaluation with temporal differences: a survey and comparison": ["machine learning", "modal and temporal logics", "policy evaluation", "reinforcement learning", "temporal differences", "value function estimation"], "Active learning using smooth relative regret approximations with applications": ["active learning", "approximation", "approximation algorithms analysis", "clustering with side information", "disagreement coeffcient", "learning to rank from pairwise preferences", "machine learning", "semi-supervised clustering", "smooth relative regret approximation"], "An extension of slow feature analysis for nonlinear blind source separation": ["feature selection", "independent component analysis", "machine learning", "nonlinear blind source separation", "slow feature analysis", "slowness principle", "statistical independence"], "Natural evolution strategies": ["black-box optimization", "design and analysis of algorithms", "evolution strategies", "machine learning", "mathematical optimization", "natural gradient", "numerical analysis", "sampling", "stochastic search"], "Conditional random field with high-order dependencies for sequence labeling and segmentation": ["conditional random field", "high-order feature", "label sparsity", "machine learning", "probability and statistics", "scheduling algorithms", "segmentation", "semi-markov conditional random field", "sequence labeling", "sequential decision making"], "Ellipsoidal rounding for nonnegative matrix factorization under noisy separability": ["computations on matrices", "document clustering", "enclosing ellipsoid", "machine learning", "nonnegative matrix factorization", "numerical analysis", "robustness to noise", "separability"], "Improving prediction from dirichlet process mixtures via enrichment": ["bayesian nonparametrics", "density regression", "expert systems", "information systems applications", "machine learning", "predictive distribution", "random partition", "urn scheme"], "Gibbs max-margin topic models with data augmentation": ["gibbs classifiers", "machine learning", "max-margin learning", "model development and analysis", "regularized bayesian inference", "supervised topic models", "support vector machines"], "A reliable effective terascale linear learning system": ["allreduce", "computations on matrices", "distributed l-bfgs", "distributed machine learning", "expert systems", "hadoop", "information systems applications", "linear algebra algorithms", "machine learning", "repeated online averaging"], "New learning methods for supervised and unsupervised preference aggregation": ["collaborative filtering", "learning-to-rank", "meta-search", "preference aggregation"], "Prediction and clustering in signed networks: a local to global perspective": ["balance theory", "cluster analysis", "graph clustering", "low rank model", "machine learning", "matrix completion", "sign prediction", "signed networks"], "Bayesian nonparametric comorbidity analysis of psychiatric disorders": ["bayesian nonparametrics", "categorical observations", "design and analysis of algorithms", "indian buffet process", "laplace approximation", "machine learning", "multinomial-logit function", "numerical analysis", "probability and statistics", "variational inference"], "Robust near-separable nonnegative matrix factorization using linear optimization": ["convex optimization", "design and analysis of algorithms", "hyperspectral unmixing", "linear programming", "machine learning", "mathematical optimization", "nonnegative matrix factorization", "numerical analysis", "pure-pixel assumption", "robustness to noise", "separability"], "Follow the leader if you can, hedge if you must": ["hedge", "learning rate", "machine learning", "mixability", "online learning", "prediction with expert advice", "stochastic control and optimization", "stochastic processes"], "Structured prediction via output space search": ["cost function", "imitation learning", "machine learning", "sorting and searching", "state space search", "structured prediction"], "Fully simplified multivariate normal updates in non-conjugate variational message passing": ["bayesian computing", "design and analysis of algorithms", "graphical models", "machine learning", "matrix differential calculus", "mean field variational bayes", "multivariate statistics", "numerical analysis", "variational approximation"], "Towards ultrahigh dimensional feature selection for big data": ["big data", "feature generation", "feature selection", "feature selection", "machine learning", "multiple kernel learning", "nonlinear feature selection", "ultrahigh dimensionality"], "Adaptive sampling for large scale boosting": ["boosting", "classification and regression trees", "feature selection", "large scale learning", "machine learning", "supervised learning by classification"], "Manopt, a matlab toolbox for optimization on manifolds": ["machine learning", "mathematical optimization", "mathematical software", "non convex", "nonlinear programming", "optimization with symmetries", "orthogonality constraints", "rank constraints", "riemannian optimization", "rotation matrices"], "Training highly multiclass classifiers": ["classification", "classification and regression trees", "large-scale", "machine learning", "multiclass", "online learning", "stochastic gradient", "supervised learning by classification"], "Locally adaptive factor processes for multivariate time series": ["bayesian nonparametrics", "locally varying smoothness", "machine learning", "multivariate statistics", "multivariate time series", "nested gaussian process", "stochastic volatility", "time series analysis"], "Iteration complexity of feasible descent methods for convex optimization": ["complexity classes", "computational complexity and cryptography", "convergence rate", "convex optimization", "convex optimization", "feasible descent methods", "iteration complexity", "machine learning"], "High-dimensional covariance decomposition into sparse Markov and independence models": ["convex optimization", "high-dimensional covariance estimation", "machine learning", "markov decision processes", "markov networks", "markov processes", "model development and analysis", "sparse covariance models", "sparse graphical model selection", "sparsistency"], "The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo": ["adaptive monte carlo", "bayesian inference", "dual averaging", "hamiltonian monte carlo", "machine learning", "markov chain monte carlo", "markov-chain monte carlo methods", "probabilistic algorithms", "sequential monte carlo methods", "simulation types and techniques"], "Confidence intervals for random forests: the jackknife and the infinitesimal jackknife": ["bagging", "graph theory", "jackknife methods", "machine learning", "monte carlo noise", "variance estimation"], "Surrogate regret bounds for bipartite ranking via strongly proper losses": ["area under roc curve", "bipartite ranking", "graph algorithms", "graph enumeration", "machine learning", "proper losses", "regret bounds", "statistical consistency", "strongly proper losses"], "Adaptive minimax regression estimation over sparse lq-hulls": ["approximation", "approximation algorithms analysis", "canonical correlation analysis", "high-dimensional sparse learning", "machine learning", "minimax rate of convergence", "model selection", "optimal aggregation", "regression analysis", "sparse lq-constraint"], "Graph estimation from multi-attribute data": ["graph theory", "graphical model selection", "machine learning", "multi-attribute data", "network analysis", "partial canonical correlation"], "Hitting and commute times in large random neighborhood graphs": ["commute distance", "graph theory", "k-nearest neighbor graph", "machine learning", "probability and statistics", "random graph", "resistance", "spectral gap"], "Bayesian inference with posterior regularization and applications to infinite latent SVMs": ["bayesian inference", "bayesian nonparametrics", "cellular neural networks", "classification", "large-margin learning", "machine learning", "machine learning theory", "multi-task learning", "neural networks", "posterior regularization", "probability and statistics"], "Expectation propagation for neural networks with sparsity-promoting priors": ["automatic relevance determination", "cellular neural networks", "expectation propagation", "linear model", "machine learning", "machine learning theory", "multilayer perceptron", "neural network", "neural networks", "sparse prior"], "Pattern alternating maximization algorithm for missing data in high-dimensional problems": ["e- and m-step", "lasso", "missing data", "observed likelihood", "penalized variational free energy"], "Dropout: a simple way to prevent neural networks from overfitting": ["deep learning", "model combination", "neural networks", "regularization"], "Sparse factor analysis for learning and content analytics": ["bayesian latent factor analysis", "factor analysis", "personalized learning", "sparse logistic regression", "sparse probit regression"], "Causal discovery with continuous additive noise models": ["additive noise", "bayesian networks", "causal inference", "causal minimality", "identifiability", "structural equation models"], "PyStruct: learning structured prediction in python": ["conditional random fields", "python", "structural support vector machines", "structured prediction"], "The student-t mixture as a natural image patch prior with application to image compression": ["density modeling", "gmm", "image compression", "mixture models", "unsupervised learning"], "Parallel MCMC with generalized elliptical slice sampling": ["approximate inference", "elliptical slice sampling", "markov chain monte carlo", "parallelism", "slice sampling"], "Classifier cascades and trees for minimizing feature evaluation cost": ["budgeted learning", "feature cost sensitive learning", "resource efficient machine learning", "tree of classifiers", "web-search ranking"], "Particle gibbs with ancestor sampling": ["bayesian inference", "non-markovian models", "particle markov chain monte carlo", "sequential monte carlo", "state-space models"], "Ramp loss linear programming support vector machine": ["generalization error analysis", "global optimization", "l1-regularization", "ramp loss", "support vector machine"], "Clustering partially observed graphs via convex optimization": ["convex optimization", "graph clustering", "sparse and low-rank decomposition"], "A tensor approach to learning mixed membership community models": ["community detection", "mixed membership models", "moment-based estimation", "spectral methods", "tensor methods"], "Cover tree Bayesian reinforcement learning": ["bayesian inference", "non-parametric statistics", "reinforcement learning"], "Efficient state-space inference of periodic latent force models": ["gaussian processes", "kalman filter", "kernel principle component analysis", "latent force models", "queueing theory"], "Spectral learning of latent-variable PCFGs: algorithms and sample complexity": ["latent-variable pcfgs", "spectral learning algorithms"], "On multilabel classification and ranking with bandit feedback": ["contextual bandits", "generalized linear", "online learning", "ranking", "regret bounds", "structured prediction"], "Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization": ["convex optimization", "online learning", "regret minimization", "stochastic gradient descent"], "One-shot-learning gesture recognition using HOG-HOF features": ["chalearn", "dynamic time warping", "histogram of optical flow", "histogram of oriented gradients"], "Contextual bandits with similarity information": ["contextual bandits", "lipschitz-continuity", "metric space", "multi-armed bandits", "regret"], "Boosting algorithms for detector cascade learning": ["boosting", "complexity-constrained learning", "cost-sensitive learning", "detector cascades", "ensemble methods", "real-time object detection", "sequential decision-making"], "Efficient and accurate methods for updating generalized linear models with multiple feature additions": ["feature selection", "group lasso", "lasso", "linear regression", "logistic regressions", "manufacturing"], "Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions": ["bayesian networks", "estimation of causal direction", "latent confounding variables", "non-gaussianity", "structural equation models"], "A truncated EM approach for spike-and-slab sparse coding": ["approximate em", "denoising", "source separation", "sparse coding", "spike-and-slab distributions", "unsupervised learning", "variational bayes"], "Efficient occlusive components analysis": ["expectation truncation", "generative models", "occlusion", "sparse coding", "unsupervised learning"], "Optimality of graphlet screening in high dimensional variable selection": ["asymptotic minimaxity", "graph of least favorables", "graph of strong dependence", "graphlet screening", "hamming distance", "phase diagram", "rare and weak signal model", "screen and clean", "sparsity"], "Tensor decompositions for learning latent variable models": ["latent variable models", "method of moments", "mixture models", "power method", "tensor decompositions", "topic models"], "Bayesian entropy estimation for countable discrete distributions": ["bayesian estimation", "bayesian nonparametrics", "dirichlet process", "entropy", "information theory", "neural coding", "pitman-yor process"], "Confidence intervals and hypothesis testing for high-dimensional regression": ["bias of an estimator", "confidence intervals", "high-dimensional models", "hypothesis testing", "lasso"], "QUIC: quadratic approximation for sparse inverse covariance estimation": ["covariance", "gaussian markov random field", "graphical model", "optimization", "regularization"], "Multimodal learning with deep Boltzmann machines": ["boltzmann machines", "deep learning", "multimodal learning", "neural networks", "unsupervised learning"], "Optimal data collection for informative rankings expose well-connected graphs": ["active learning", "algebraic connectivity", "graph synthesis", "optimal experimental design", "ranking", "scheduling"], "Bayesian co-boosting for multi-modal gesture recognition": ["bayesian co-boosting", "feature selection", "gesture recognition", "hidden markov model", "multimodal fusion"], "Effective string processing and matching for author disambiguation": ["author disambiguation", "deduplication", "name matching"], "High-dimensional learning of linear causal networks via inverse covariance estimation": ["causal inference", "dynamic programming", "identifiability", "inverse covariance matrix estimation", "linear structural equation models"], "Recursive teaching dimension, VC-dimension and sample compression": ["combinatorial parameters", "compression schemes", "recursive teaching", "tail matching algorithm", "upper bounds", "vapnik-chervonenkis dimension"], "Do we need hundreds of classifiers to solve real world classification problems?": ["bayesian classifiers", "classification", "decision trees", "discriminant analysis", "ensembles", "generalized linear models", "logistic and multinomial regression", "multiple adaptive regression splines", "nearest-neighbors", "neural networks", "partial least squares and principal component regression", "random forest", "rule-based classifiers", "support vector machine", "uci data base"], "ooDACE toolbox: a flexible object-oriented Kriging implementation": ["blind kriging", "co-kriging", "dace", "gaussian process", "kriging", "metamodeling", "surrogate modeling"], "Robust online gesture recognition with crowdsourced annotations": ["accelerometer sensors", "crowdsourced annotation", "gesture spotting", "longest common subsequence", "template matching methods"], "Accelerating t-SNE using tree-based algorithms": ["barnes-hut algorithm", "dual-tree algorithm", "embedding", "multidimensional scaling", "space-partitioning trees", "t-sne"], "Set-valued approachability and online learning with partial monitoring": ["approachability", "online learning", "partial monitoring", "regret"], "Learning graphical models with hubs": ["alternating direction method of multipliers", "binary network", "covariance graph", "gaussian graphical model", "hub", "lasso"], "Inconsistency of Pitman-Yor process mixtures for the number of components": ["bayesian nonparametrics", "consistency", "dirichlet process mixture", "finite mixture", "number of components"], "Active contextual policy search": ["active learning", "movement primitives", "multi-task learning", "policy search", "reinforcement learning"], "Matrix completion with the trace norm: learning, bounding, and transducing": ["collaborative filtering", "matrix completion", "sample complexity", "trace-norm regularization", "transductive learning"], "Statistical analysis of metric graph reconstruction": ["filament", "manifold learning", "metric graph", "minimax estimation", "reconstruction"], "Alternating linearization for structured regularization problems": ["fused lasso", "lasso", "nonsmooth optimization", "operator splitting"], "The gesture recognition toolkit": ["c++", "classification", "clustering", "feature extraction", "gesture recognition", "gesture spotting", "machine learning", "open source", "regression", "signal processing"], "Convolutional nets and watershed cuts for real-time semantic Labeling of RGBD videos": ["convolutional networks", "deep learning", "depth information", "optimization", "superpixels"], "On the bayes-optimality of F-measure maximizers": ["algorithms", "bayes-optimal predictions", "design", "experimentation", "expert systems", "f-measure", "information systems applications", "machine learning", "measurement", "multi-label classification", "performance", "regret", "statistical decision theory", "statistical graphics", "statistical paradigms", "structured output prediction"], "SPMF: a Java open-source pattern mining library": ["algorithms", "data mining", "design", "experimentation", "frequent pattern mining", "library", "machine learning", "measurement", "open-source", "performance", "sequence database", "transaction database"], "Efficient learning and planning with compressed predictive states": ["algorithms", "design", "dimensionality reduction", "experimentation", "expert systems", "information systems applications", "machine learning", "measurement", "performance", "planning and scheduling", "predictive state representation", "random projections", "reinforcement learning"], "Revisiting Stein's paradox: multi-task averaging": ["algorithms", "design", "experimentation", "expert systems", "information systems applications", "james-stein", "knowledge representation and reasoning", "logic", "machine learning", "measurement", "multi-task learning", "performance", "stein's paradox"], "Multi-objective reinforcement learning using sets of pareto dominating policies": ["algorithms", "design", "experimentation", "hypervolume", "knowledge representation and reasoning", "logic", "machine learning", "multi-objective", "multiple criteria analysis", "pareto sets", "reinforcement learning", "theorem proving algorithms"], "Seeded graph matching for correlated Erd\u00f6s-R\u00e9nyi graphs": ["algorithms", "assignment problem", "canonical correlation analysis", "consistency", "design", "erdos-r\u00e9nyi graph", "estimation", "experimentation", "frank-wolfe", "graph matching", "graph theory", "machine learning", "measurement", "performance", "regression analysis", "seeded vertices"], "Asymptotic accuracy of distribution-based estimation of latent variables": ["algorithms", "bayes method", "design", "distribution functions", "experimentation", "hierarchical parametric models", "latent variable", "machine learning", "maximum likelihood method", "measurement", "performance", "unsupervised learning"], "What regularized auto-encoders learn from the data-generating distribution": ["algorithms", "auto-encoders", "denoising auto-encoders", "design", "distribution functions", "experimentation", "generative models", "machine learning", "manifold learning", "markov chains", "measurement", "performance", "score matching", "unsupervised representation learning"], "Revisiting Bayesian blind deconvolution": ["algorithms", "blind deconvolution", "blind image deblurring", "design", "experimentation", "machine learning", "measurement", "performance", "sparse estimation", "sparse priors", "statistical graphics", "statistical paradigms", "variational bayes"], "New results for random walk learning": ["algorithms", "computational learning theory", "design", "dnf learning", "experimentation", "fourier analysis of boolean functions", "machine learning", "measurement", "paths and connectivity problems", "performance", "random walks", "top learning"], "Transfer learning decision forests for gesture recognition": ["algorithms", "computer vision", "computer vision problems", "decision forests", "design", "experimentation", "gesture recognition", "machine learning", "measurement", "performance", "scene understanding", "transfer learning"], "Semi-supervised eigenvectors for large-scale locally-biased learning": ["algorithms", "design", "experimentation", "kernel methods", "large-scale machine learning", "local spectral methods", "locally-biased learning", "machine learning", "measurement", "performance", "semi-supervised learning", "spectral clustering", "statistical graphics", "statistical paradigms"], "BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits": ["algorithms", "bayesian optimization", "design", "efficient global optimization", "experimentation", "gaussian processes", "machine learning", "mathematical optimization", "measurement", "performance", "sequential experimental design", "sequential model-based optimization"], "Order-independent constraint-based causal structure learning": ["algorithms", "ccd-algorithm", "consistency", "constraint and logic programming", "design", "directed acyclic graph", "experimentation", "fci-algorithm", "high-dimensional data", "machine learning", "mathematical optimization", "measurement", "order-dependence", "pc-algorithm", "performance"], "Effective sampling and learning for mallows models with pairwise-preference data": ["algorithms", "design", "experimentation", "incomplete data", "machine learning", "machine learning approaches", "mallows models", "measurement", "mixture models", "performance", "preference learning", "ranking"], "Robust hierarchical clustering": ["agglomerative algorithms", "algorithms", "cluster analysis", "clustering", "design", "experimentation", "machine learning", "measurement", "performance", "robustness", "unsupervised learning"], "Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization": ["active learning", "algorithms", "batch", "design", "experimentation", "expert systems", "gaussian process", "information systems applications", "machine learning", "mathematical optimization", "measurement", "performance", "regret bound", "upper confidence bound"], "Active lmitation learning: formal and practical reductions to I.I.D. learning": ["active imitation learning", "active learning", "algorithms", "design", "experimentation", "expert systems", "imitation learning", "information systems applications", "machine learning", "measurement", "performance", "reductions"]}, {"J\u00fcri Lember": "Department of Mathematics, Royal Holloway University of London, Egham, UK", "Manu Nandan": "Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL", "Pramod P. Khargonekar": "Qualcomm Research Center, San Diego, CA", "Richard Oentaryo": "Living Analytics Research Centre, Singapore Management University, Singapore", "Ee-Peng Lim": "Living Analytics Research Centre, Singapore Management University, Singapore", "Michael Finegold": "Living Analytics Research Centre, Singapore Management University, Singapore", "David Lo": "Living Analytics Research Centre, Singapore Management University, Singapore", "Feida Zhu": "SAS Institute Pte. Ltd., Singapore", "Eng-Yeow Cheu": "Data Analytics Department, Institute for Infocomm Research, Singapore", "Ghim-Eng Yap": "Data Analytics Department, Institute for Infocomm Research, Singapore", "Kelvin Sim": "Data Analytics Department, Institute for Infocomm Research, Singapore", "Minh Nhut Nguyen": "Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates", "Kasun Perera": "Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates", "Bijay Neupane": "Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates", "Mustafa Faisal": "Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates", "Zeyar Aung": "Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates", "Wei Lee Woon": "Institute for Infocomm Research, Singapore", "Wei Chen": "Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India", "Dhaval Patel": "Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan", "Marc Claesen": "KU Leuven, Department of Public Health and Primary Care, Environment and Health, Leuven, Belgium", "Frank De Smet": "KU Leuven, Leuven, Belgium", "Johan A. K. Suykens": "KU Leuven, Leuven, Belgium", "Divyanshu Vats": "Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI", "Twan Van Laarhoven": "Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands", "Aleksandr Aravkin": "Department of Mathematics, University of Washington, Seattle, WA", "James V. Burke": "Department of Information Engineering, University of Padova, Padova, Italy", "Alessandro Chiuso": "Department of Information Engineering, University of Padova, Padova, Italy", "Aaron Wilson": "School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR", "Alan Fern": "School of EECS, Oregon State University, Corvallis, OR", "Matthieu Geist": "INRIA Lorraine, Villers-l\u00e8s-Nancy, France", "Garvesh Raskutti": "Department of Statistics, University of California, Berkeley, CA", "Martin J. Wainwright": "Department of Statistics, University of California, Berkeley, CA", "Patrick Fox-Roberts": "Computer Vision Consulting, London, UK", "Karthik Mohan": "Department of Computer Science and Engineering, Genome Sciences, University of Washington, Seattle, WA", "Palma London": "Department of Electrical Engineering, University of Washington, Seattle, WA", "Maryam Fazel": "Department of Biostatistics, University of Washington, Seattle, WA", "Daniela Witten": "Departments of Computer Science and Engineering, Genome Sciences, University of Washington, Seattle WA", "Haotian Pang": "Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ", "Han Liu": "Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania", "Steven C. H. Hoi": "School of Computer Engineering, Nanyang Technological University, Singapore", "Jialei Wang": "School of Computer Engineering, Nanyang Technological University, Singapore", "Daniel Lowd": "Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium", "Marco Cuturi": "Graduate School of Informatics, Kyoto University, Kyoto, Japan", "Emile Richard": "Ecole Polytechnique, Palaiseau Cedex, France", "St\u00e9phane Ga\u00efffas": "ENS Cachan, UMR, CNRS, Cachan Cedex, France", "Rajen Dinesh Shah": "Seminar f\u00fcr Statistik, ETH Z\u00fcrich, Z\u00fcrich, Switzerland", "Brett L. Moore": "Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD", "Larry D. Pyeatt": "Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA", "Vivekanand Kulkarni": "Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA", "Periklis Panousis": "Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA", "Kevin Padrez": "Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA", "Emanuele Coviello": "Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong", "Antoni B. Chan": "Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA", "Teng Zhang": "School of Mathematics, University of Minnesota, Minneapolis, MN", "Christoph Dann": "Technische Universit\u00e4t Darmstadt, Darmstadt, Germany", "Gerhard Neumann": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany and Technische Universit\u00e4t Darmstadt, Darmstadt, Germany", "Nir Ailon": "Department of Computer Science, Technion Israel Institute of Technology, Haifa, Israel", "Ron Begleiter": "Coutrant Institute of Mathematical Science, New York University, New York, NY", "Henning Sprekeler": "Institute for Theoretical Biology and Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany", "Tiziano Zito": "Institut f\u00fcr Neuroinformatik, Ruhr-Universit\u00e4t Bochum, Germany and Institute for Theoretical Biology and Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universit\u00e4t ...", "Tobias Glasmachers": "Google Inc., Mountain View, United States", "Yi Sun": "Intelligent Autonomous Systems Institute, Technische Universit\u00e4t Darmstadt, Germany", "Jan Peters": "Istituto Dalle Molle di Studi sull'Intelligenza Articiale, University of Lugano, Manno-Lugano, Switzerland", "Nguyen Viet Cuong": "Department of Computer Science, National University of Singapore, Singapore", "Nan Ye": "Department of Computer Science, National University of Singapore, Singapore", "Wee Sun Lee": "DSO National Laboratories, Singapore", "Sara Wade": "Department of Statistical Science, Duke University, Durham, NC", "David B. Dunson": "Department of Decision Sciences, Bocconi University, Milan, Italy", "Sonia Petrone": "Department of Biostatistics, Harvard University, Boston, MA", "Jun Zhu": "Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, ...", "Ning Chen": "School of Computer Science, Carnegie Mellon University, Pittsburgh, PA", "Hugh Perkins": "State Key Lab of Intelligent Technology and Systems, Tsinghua National Lab for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, Chin ...", "Alekh Agarwal": "Criteo, Palo Alto, CA", "Miroslav Dud\u00edk": "Microsoft Research, New York, NY", "Maksims N. Volkovs": "University of Toronto, Toronto, ON", "Kai-Yang Chiang": "Department of Computer Science, University of Texas at Austin, Austin, TX", "Cho-Jui Hsieh": "Department of Computer Sciences, University of Texas at Austin, Austin, TX", "Nagarajan Natarajan": "Department of Computer Science, University of Texas at Austin, Austin, TX", "Inderjit S. Dhillon": "Department of Computer Sciences, University of Texas at Austin, Austin, TX", "Francisco J. R. Ruiz": "Department of Signal Processing and Communications, University Carlos III in Madrid, Legan\u00e9es, Madrid, Spain", "Isabel Valera": "Department of Psychiatry, New York State Psychiatric Institute, Columbia University, New York, NY", "Carlos Blanco": "Department of Signal Processing and Communications, University Carlos III in Madrid, Legan\u00e9s, Madrid, Spain", "Nicolas Gillis": "Institut f\u00fcr Mathematik, Technische Universit\u00e4t Berlin, Berlin, Germany", "Steven De Rooij": "D\u00e9partement de Math\u00e9matiques, Universit\u00e9 Paris-Sud, Orsay Cedex, France", "Tim Van Erven": "Leiden University and Centrum Wiskunde & Informatica, Amsterdam, the Netherlands", "Peter D. Gr\u00fcnwald": "Leiden University and Centrum Wiskunde & Informatica, Amsterdam, the Netherlands", "Janardhan Rao Doppa": "School of EECS, Oregon State University, Corvallis, OR", "Mingkui Tan": "Center for Quantum Computation & Intelligent Systems, University of Technology Sydney, Sydney, NSW, Sydney, Australia", "Ivor W. Tsang": "Department of Mathematics, University of California, San Diego, La Jolla, CA", "Charles Dubout": "Computer Vision and Learning Group, Idiap Research Institute, Martigny, Switzerland", "Nicolas Boumal": "Department of Electrical Engineering and Computer Science, Universit\u00e9 de Li\u00e8ge, Li\u00e8ge, Belgium", "Bamdev Mishra": "Department of Mathematical Engineering, Universit\u00e9 catholique de Louvain, Louvain-la-Neuve, Belgium", "P.-A. Absil": "Department of Engineering, University of Cambridge, Cambridge, UK", "Maya R. Gupta": "Institute of Mathematics and Informatics, University of P\u00e9cs,Hungary", "Samy Bengio": "Google Inc., New York, NY", "Daniele Durante": "Department of Statistical Sciences, University of Padua, Padua, Italy", "Bruno Scarpa": "Department of Statistical Science, Duke University, Durham, NC", "Po-Wei Wang": "Department of Computer Science, National Taiwan University, Taipei, Taiwan", "Majid Janzamin": "Department of Electrical Engineering and Computer Science, University of California, Irvine, CA", "Stefan Wager": "Department of Statistics, Stanford University, Stanford, CA", "Trevor Hastie": "Department of Statistics, Stanford University, Stanford, CA", "Fuchang Gao": "School of Statistics, University of Minnesota, Minneapolis, MN", "Mladen Kolar": "Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey", "Ulrike Von Luxburg": "Institute of Mathematics, University of Leipzig, Leipzig, Germany", "Agnes Radl": "Department of Mathematics and Computer Science, Saarland University, Saarbr\u00fccken, Germany", "Pasi Jyl\u00e4nki": "Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA", "Aapo Nummenmaa": "Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Finland", "Nicolas St\u00e4dler": "Quantik AG, Berikon, Switzerland", "Nitish Srivastava": "Department of Statistics and Computer Science, University of Toronto, Toronto, Ontario, Canada", "Geoffrey Hinton": "Department of Computer Science, University of Toronto, Toronto, Ontario, Canada", "Alex Krizhevsky": "Department of Computer Science, University of Toronto, Toronto, Ontario, Canada", "Ilya Sutskever": "Department of Computer Science, University of Toronto, Toronto, Ontario, Canada", "Andrew S. Lan": "Dept. Electrical and Computer Engineering, Rice University, Houston, TX", "Andrew E. Waters": "School of Electrical and Computer Engineering, Cornell University, Ithaca, NY", "Christoph Studer": "Dept. Electrical and Computer Engineering, Rice University, Houston, TX", "Jonas Peters": "Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands and Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands", "Joris M. Mooij": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Dominik Janzing": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Andreas C. M\u00fcller": "Institute of Computer Science, Department VI, University of Bonn, Bonn, Germany", "A\u00e4ron Van Den Oord": "Department of Electronics and Information Systems, Ghent University, Ghent, Belgium", "Robert Nishihara": "School of Informatics, University of Edinburgh, Edinburgh, UK", "Iain Murray": "School of Engineering and Applied Sciences, Harvard University, Cambridge, MA", "Zhixiang Xu": "Department of Computer Science, Washington University, St. Louis, MO", "Matt J. Kusner": "Department of Computer Science, Washington University, St. Louis, MO", "Kilian Q. Weinberger": "Criteo, Palo Alto, CA", "Fredrik Lindsten": "Computer Science Division and Department of Statistics, University of California, Berkeley, CA", "Michael I. Jordan": "Department of Information Technology, Uppsala University, Uppsala, Sweden", "Xiaolin Huang": "Department of Electrical Engineering, ESAT-STADIUS, KU Leuven and School of Mathematical Sciences, Fudan University, Shanghai, P.R. China", "Lei Shi": "Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Leuven, Belgium", "Yudong Chen": "Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX", "Ali Jalali": "Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX", "Sujay Sanghavi": "Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore", "Animashree Anandkumar": "Microsoft Research, Cambridge, MA", "Rong Ge": "Department of Computer Science, Columbia University, New York, NY", "Daniel Hsu": "Microsoft Research, Cambridge, MA", "Nikolaos Tziortziotis": "Department of Computer Science and Engineering, Chalmers University of Technology, Sweden", "Christos Dimitrakakis": "Department of Computer Science and Engineering, University of Ioannina, Greece", "Steven Reece": "Electronics and Computer Science, University of Southampton, Southampton, UK", "Siddhartha Ghosh": "Electronics and Computer Science, University of Southampton, Southampton, UK", "Alex Rogers": "Department of Engineering Science, University of Oxford, Oxford, UK", "Stephen Roberts": "Electronics and Computer Science, University of Southampton, Southampton, UK and Department of Computing and Information Technology, King Abdulaziz University, Saudi Arabia", "Shay B. Cohen": "Department of Computer Science, Columbia University, New York, NY", "Karl Stratos": "Department of Computer Science, Columbia University, New York, NY", "Michael Collins": "Yahoo! Labs, New York, NY", "Dean P. Foster": "Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA", "Claudio Gentile": "Toyota Technological Institute at Chicago, Chicago, IL", "Elad Hazan": "Yahoo! Labs, New York, NY", "Jakub Kone\u010dn\u00fd": "Kore\u0161ponden\u010dn\u00fd Matematick\u00fd Semin\u00e1r, Comenius University, Bratislava, Slovakia", "Mohammad Saberian": "Statistical Visual Computing Laboratory, University of California, San Diego, La Jolla, CA", "Amit Dhurandhar": "IBM TJ Watson, Yorktown Heights, NY", "Shohei Shimizu": "Department of Sociology, University of North Carolina, Chapel Hill, NC", "Abdul-Saboor Sheikh": "Faculty of Electrical Engineering and Computer Science, Technical University Berlin, Berlin, Germany", "Jacquelyn A. Shelton": "Cluster of Excellence Hearing4all and Faculty VI, University of Oldenburg, Oldenburg, Germany", "Marc Henniges": "Department of Engineering, University of Cambridge, Cambridge, UK", "Richard E. Turner": "University College London, London, UK", "Maneesh Sahani": "Honda Research Institute Europe GmbH, Germany", "Julian Eggert": "Cluster of Excellence Hearing4all and Faculty VI, University of Oldenburg, Oldenburg, Germany and Frankfurt Institute for Advanced Studies, Goethe-University Frankfurt, Frankfurt, Germany and Elec ...", "Jiashun Jin": "Department of Statistics, Rutgers University, Piscataway, NJ", "Cun-Hui Zhang": "Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI", "Sham M. Kakade": "Department of Statistics, Rutgers University, Piscataway, NJ", "Evan Archer": "Center for Perceptual Systems, The University of Texas at Austin, Austin, TX", "Il Memming Park": "Department of Psychology, Section of Neurobiology, Division of Statistics and Scientific Computation and Center for Perceptual Systems, The University of Texas at Austin, Austin, TX", "Adel Javanmard": "Department of Electrical Engineering and Department of Statistics, Stanford University, Stanford, CA", "M\u00e1ty\u00e1s A. Sustik": "Department of Computer Sciences, University of Texas at Austin, Austin, TX", "Braxton Osting": "Department of Applied Mathematics, University of Twente, Enschede, The Netherlands", "Christoph Brune": "Department of Mathematics, University of California, Los Angeles, CA", "Jiaxiang Wu": "National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China", "Wei-Sheng Chin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Yong Zhuang": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Yu-Chin Juan": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Felix Wu": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Hsiao-Yu Tung": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Tong Yu": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Jui-Pin Wang": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Cheng-Xia Chang": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Chun-Pai Yang": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Wei-Cheng Chang": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Kuan-Hao Huang": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Tzu-Ming Kuo": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Shan-Wei Lin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Young-San Lin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Yu-Chen Lu": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Yu-Chuan Su": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Cheng-Kuang Wei": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Tu-Chun Yin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Chun-Liang Li": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Ting-Wei Lin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Cheng-Hao Tsai": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Shou-De Lin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Hsuan-Tien Lin": "Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan", "Po-Ling Loh": "Seminar f\u00fcr Statistik, ETH Z\u00fcrich, Z\u00fcrich, Switzerland", "Thorsten Doliwa": "Department of Computing Science, University of Alberta, Edmonton, AB, Canada", "Gaojian Fan": "Horst-G\u00f6rtz Institute for IT Security and Faculty of Mathematics, Ruhr-Universit\u00e4t Bochum, Bochum, Germany", "Hans Ulrich Simon": "Department of Computer Science, University of Regina, Regina, SK, Canada", "Manuel Fern\u00e1ndez-Delgado": "Centro de Investigaci\u00f3n en Tecnolox\u00edas da Informaci\u00f3n da USC, University of Santiago de Compostela, Santiago de Compostela, Spain", "Eva Cernadas": "Centro de Investigaci\u00f3n en Tecnolox\u00edas da Informaci\u00f3n da USC, University of Santiago de Compostela, Santiago de Compostela, Spain", "Sen\u00e9n Barro": "Departamento de Tecnologia e Ci\u00eancias Sociais, Universidade do Estado da Bahia, Juazeiro, Brasil", "Ivo Couckuyt": "Ghent University, iMinds, Department of Information Technology, Gent, Belgium", "Tom Dhaene": "Ghent University, iMinds, Department of Information Technology, Gent, Belgium", "Long-Van Nguyen-Dinh": "Wearable Computing Lab, ETH Z\u00fcrich, Z\u00fcrich, Switzerland", "Alberto Calatroni": "Wearable Computing Lab, ETH Z\u00fcrich, Z\u00fcrich, Switzerland", "Shie Mannor": "Universit\u00e9 Paris Diderot, LPMA, Paris, France", "Vianney Perchet": "HEC Paris, CNRS, Jouy-en-Josas, France", "Kean Ming Tan": "Department of Electrical Engineering, University of Washington, Seattle, WA", "Su-In Lee": "Department of Electrical Engineering, University of Washington, Seattle, WA", "Jeffrey W. Miller": "Division of Applied Mathematics, Brown University, Providence, RI", "Alexander Fabisch": "Robotics Research Group, University Bremen, Bremen, Germany", "Ohad Shamir": "School of Computer Science and Engineering, The Hebrew University, Givat Ram, Jerusalem, Israel", "Fabrizio Lecci": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Alessandro Rinaldo": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Xiaodong Lin": "Statistical and Applied Mathematical Sciences Institute, Durham, NC", "Nicholas Gillian": "Responsive Environments Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA", "Camille Couprie": "Twitter, Inc., San Francisco, CA", "Cl\u00e9ment Farabet": "Universit\u00e9 Paris-Est, Laboratoire d'Informatique Gaspard-Monge, Paris, France", "Laurent Najman": "New York University & Facebook AI Research, Courant Institute of Mathematical Sciences, New York, NY", "Willem Waegeman": "Institute of Computing Science, Poznan University of Technology, Poznan, Poland", "Krzysztof Dembczy\u0144ki": "Institute of Computing Science, Poznan University of Technology, Poznan, Poland", "Arkadiusz Jachnik": "Amazon Development Center Germany, Berlin, Germany", "Philippe Fournier-Viger": "Department of Information and Communication Engineering, University of Murcia, Murcia, Spain", "Antonio Gomariz": "Department of Computer Engineering, Ferdowsi University of Mashhad, Iran", "Ted Gueniche": "Department of Computer Engineering, Ferdowsi University of Mashhad, Iran", "Azadeh Soltani": "Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan", "Cheng-Wei Wu": "Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan", "William Hamilton": "School of Computer Science, McGill University, Montreal, QC, Canada", "Mahdi Milani Fard": "School of Computer Science, McGill University, Montreal, QC, Canada", "Kristof Van Moffaert": "Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium", "Vince Lyzinski": "Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD", "Donniell E. Fishkind": "Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD", "Guillaume Alain": "Department of Computer Science and Operations Research, University of Montreal, Montreal, Quebec, Canada", "David Wipf": "School of Computer Science, Northwestern Polytechnical University, Xi'an, P.R. China", "Jeffrey C. Jackson": "Duquesne University, Pittsburgh, PA", "Norberto A. Goussies": "Departamento de Computaci\u00f3n, Pabell\u00f3n I Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina", "Sebasti\u00e1n Ubalde": "Departamento de Computaci\u00f3n, Pabell\u00f3n I Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina", "Toke J. Hansen": "International Computer Science Institute and Dept. of Statistics, University of California, Berkeley, CA", "Diego Colombo": "Seminar for Statistics, ETH Zurich, Zurich, Switzerland", "Tyler Lu": "Department of Computer Science, University of Toronto, Toronto, ON, Canada", "Maria-Florina Balcan": "Department of Computer Science, Princeton University, Princeton, NJ", "Yingyu Liang": "Google, Inc., Mountain View, CA", "Thomas Desautels": "Department of Computer Science, ETH Zurich, Z\u00fcrich, Switzerland", "Andreas Krause": "Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA", "Kshitij Judah": "School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR", "Alan P. Fern": "School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR", "Thomas G. 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