jmlr_2017.json

[{"Averaged collapsed variational bayes inference": ["Katsuhiko Ishiguro", "Issei Sato", "Naonori Ueda"], "Scalable inuence maximization for multiple products in continuous-time diffusion networks": ["Nan Du", "Yingyu Liang", "Maria-Florina Balcan", "Manuel Gomez-Rodriguez", "Hongyuan Zha", "Le Song"], "Local algorithms for interactive clustering": ["Pranjal Awasthi", "Maria Florina Balcan", "Konstantin Voevodski"], "SnapVX: a network-based convex optimization solver": ["David Hallac", "Christopher Wong", "Steven Diamond", "Abhijit Sharang", "Rok Sosic", "Stephen Boyd", "Jure Leskovec"], "Communication-efficient sparse regression": ["Jason D. Lee", "Qiang Liu", "Yuekai Sun", "Jonathan E. Taylor"], "Improving variational methods via pairwise linear response identities": ["Jack Raymond", "Federico Ricci-Tersenghi"], "Distributed sequence memory of multidimensional inputs in recurrent networks": ["Adam S. Charles", "Dong Yin", "Christopher J. Rozell"], "Persistence images: a stable vector representation of persistent homology": ["Henry Adams", "Tegan Emerson", "Michael Kirby", "Rachel Neville", "Chris Peterson", "Patrick Shipman", "Sofya Chepushtanova", "Eric Hanson", "Francis Motta", "Lori Ziegelmeier"], "Spectral clustering based on local PCA": ["Ery Arias-Castro", "Gilad Lerman", "Teng Zhang"], "On perturbed proximal gradient algorithms": ["Yves F. Atchad\u00e9", "Gersende Fort", "Eric Moulines"], "Differential privacy for bayesian inference through posterior sampling": ["Christos Dimitrakakis", "Blaine Nelson", "Zuhe Zhang", "Aikaterini Mitrokotsa", "Benjamin I. P. Rubinstein"], "Refinery: an open source topic modeling web platform": ["Daeil Kim", "Benjamin F. Swanson", "Michael C. Hughes", "Erik B. Sudderth"], "Using conceptors to manage neural long-term memories for temporal patterns": ["Herbert Jaeger"], "Automatic differentiation variational inference": ["Alp Kucukelbir", "Dustin Tran", "Rajesh Ranganath", "Andrew Gelman", "David M. Blei"], "Empirical evaluation of resampling procedures for optimising SVM hyperparameters": ["Jacques Wainer", "Gavin Cawley"], "A unified formulation and fast accelerated proximal gradient method for classification": ["Naoki Ito", "Akiko Takeda", "Kim-Chuan Toh"], "Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning": ["Guillaume Lema\u00eetre", "Fernando Nogueira", "Christos K. Aridas"], "Information-geometric optimization algorithms: a unifying picture via invariance principles": ["Yann Ollivier", "Ludovic Arnold", "Anne Auger", "Nikolaus Hansen"], "Breaking the curse of dimensionality with convex neural networks": ["Francis Bach"], "Memory efficient kernel approximation": ["Si Si", "Cho-Jui Hsieh", "Inderjit S. Dhillon"], "On the equivalence between kernel quadrature rules and random feature expansions": ["Francis Bach"], "Analyzing tensor power method dynamics in overcomplete regime": ["Animashree Anandkumar", "Rong Ge", "Majid Janzamin"], "JSAT: Java statistical analysis tool, a library for machine learning": ["Edward Raff"], "Identifying a minimal class of models for high-dimensional data": ["Daniel Nevo", "Ya'acov Ritov"], "Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA": ["Lars Kotthoff", "Chris Thornton", "Holger H. Hoos", "Frank Hutter", "Kevin Leyton-Brown"], "POMDPs.jl: a framework for sequential decision making under uncertainty": ["Maxim Egorov", "Zachary N. Sunberg", "Edward Balaban", "Tim A. Wheeler", "Jayesh K. Gupta", "Mykel J. Kochenderfer"], "Generalized p\u00f3ya urn for time-varying pitman-yor processes": ["Fran\u00e7ois Caron", "Willie Neiswanger", "Frank Wood", "Arnaud Doucet", "Manuel Davy"], "Particle gibbs split-merge sampling for Bayesian inference in mixture models": ["Alexandre Bouchard-C\u00f4t\u00e9", "Arnaud Doucet", "Andrew Roth"], "Certifiably optimal low rank factor analysis": ["Dimitris Bertsimas", "Martin S. Copenhaver", "Rahul Mazumder"], "Group sparse optimization via lp,q regularization": ["Yaohua Hu", "Chong Li", "Kaiwen Meng", "Jing Qin", "Xiaoqi Yang"], "Preference-based teaching": ["Ziyuan Gao", "Christoph Ries", "Hans U. Simon", "Sandra Zilles"], "Nonparametric risk bounds for time-series forecasting": ["Daniel J. McDonald", "Cosma Rohilla Shalizi", "Mark Schervish"], "Online Bayesian passive-aggressive learning": ["Tianlin Shi", "Jun Zhu"], "Asymptotic analysis of objectives based on fisher information in active learning": ["Jamshid Sourati", "Murat Akcakaya", "Todd K. Leen", "Deniz Erdogmus", "Jennifer G. Dy"], "A spectral algorithm for inference in hidden semi-Markov models": ["Igor Melnyk", "Arindam Banerjee"], "Simplifying probabilistic expressions in causal inference": ["Santtu Tikka", "Juha Karvanen"], "Nearly optimal classification for semimetrics": ["Lee-Ad Gottlieb", "Aryeh Kontorovich", "Pinhas Nisnevitch"], "Bridging supervised learning and test-based co-optimization": ["Elena Popovici"], "GFA: exploratory analysis of multiple data sources with group factor analysis": ["Eemeli Lepp\u00e4aho", "Muhammad Ammad-ud-din", "Samuel Kaski"], "GPflow: a Gaussian process library using tensorflow": ["Alexander G. De G. Matthews", "Mark Van Der Wilk", "Tom Nickson", "Keisuke Fujii", "Alexis Boukouvalas", "Pablo Le\u00f3n-Villagr\u00e1", "Zoubin Ghahramani", "James Hensman"], "COEVOLVE: a joint point process model for information diffusion and network evolution": ["Mehrdad Farajtabar", "Yichen Wang", "Manuel Gomez-Rodriguez", "Shuang Li", "Hongyuan Zha", "Le Song"], "Learning local dependence in ordered data": ["Guo Yu", "Jacob Bien"], "Bayesian learning of dynamic multilayer networks": ["Daniele Durante", "Nabanita Mukherjee", "Rebecca C. Steorts"], "Time-accuracy tradeoffs in kernel prediction: controlling prediction quality": ["Samory Kpotufe", "Nakul Verma"], "Asymptotic behavior of support vector machine for spiked population model": ["Hanwen Huang"], "Distributed semi-supervised learning with kernel ridge regression": ["Xiangyu Chang", "Shao-Bo Lin", "Ding-Xuan Zhou"], "On Markov chain Monte Carlo methods for tall data": ["R\u00e9mi Bardenet", "Arnaud Doucet", "Chris Holmes"], "Explaining the success of adaboost and random forests as interpolating classifiers": ["Abraham J. Wyner", "Matthew Olson", "Justin Bleich", "David Mease"], "Clustering from general pairwise observations with applications to time-varying graphs": ["Shiau Hong Lim", "Yudong Chen", "Huan Xu"], "Uniform hypergraph partitioning: provable tensor methods and sampling techniques": ["Debarghya Ghoshdastidar", "Ambedkar Dukkipati"], "Reconstructing undirected graphs from eigenspaces": ["Yohann De Castro", "Thibault Espinasse", "Paul Rochet"], "An optimal algorithm for bandit and zero-order convex optimization with two-point feedback": ["Ohad Shamir"], "Perishability of data: dynamic pricing under varying-coefficient models": ["Adel Javanmard"], "Two new approaches to compressed sensingexhibiting both robust sparse recovery and the grouping effect": ["Mehmet Eren Ahsen", "Niharika Challapalli", "Mathukumalli Vidyasagar"], "On the consistency of ordinal regression methods": ["Fabian Pedregosa", "Francis Bach", "Alexandre Gramfort"], "Statistical inference with unnormalized discrete models and localized homogeneous divergences": ["Takashi Takenouchi", "Takafumi Kanamori"], "Density estimation in infinite dimensional exponential families": ["Bharath Sriperumbudur", "Kenji Fukumizu", "Arthur Gretton", "Aapo Hyv\u00e4rinen", "Revant Kumar"], "Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis": ["Matth\u00e4us Kleindessner", "Ulrike Von Luxburg"], "Joint label inference in networks": ["Deepayan Chakrabarti", "Stanislav Funiak", "Jonathan Chang", "Sofus A. Macskassy"], "Achieving optimal misclassification proportion in stochastic block models": ["Chao Gao", "Zongming Ma", "Anderson Y. Zhang", "Harrison H. Zhou"], "On the propagation of low-rate measurement error to subgraph counts in large networks": ["Prakash Balachandran", "Eric D. Kolaczyk", "Weston D. Viles"], "Dense distributions from sparse samples: improved gibbs sampling parameter estimators for LDA": ["Yannis Papanikolaou", "James R. Foulds", "Timothy N. Rubin", "Grigorios Tsoumakas"], "Fundamental conditions for low-CP-rank tensor completion": ["Morteza Ashraphijuo", "Xiaodong Wang"], "Parallel symmetric class expression learning": ["An C. Tran", "Jens Dietrich", "Hans W. Guesgen", "Stephen Marsland"], "Learning partial policies to speedup MDP tree search via reduction to I.I.D. learning": ["Jervis Pinto", "Alan Fern"], "Hierarchically compositional kernels for scalable nonparametric learning": ["Jie Chen", "Haim Avron", "Vikas Sindhwani"], "Sharp oracle inequalities for square root regularization": ["Benjamin Stucky", "Sara Van De Geer"], "Soft margin support vector classification as buffered probability minimization": ["Matthew Norton", "Alexander Mafusalov", "Stan Uryasev"], "Variational particle approximations": ["Ardavan Saeedi", "Tejas D. Kulkarni", "Vikash K. Mansinghka", "Samuel J. Gershman"], "A Bayesian framework for learning rule sets for interpretable classification": ["Tong Wang", "Cynthia Rudin", "Finale Doshi-Velez", "Yimin Liu", "Erica Klampfl", "Perry MacNeille"], "A robust-equitable measure for feature ranking and selection": ["A. Adam Ding", "Jennifer G. Dy", "Yi Li", "Yale Chang"], "Multiscale strategies for computing optimal transport": ["Samuel Gerber", "Mauro Maggioni"], "Non-parametric policy search with limited information loss": ["Herke Van Hoof", "Gerhard Neumann", "Jan Peters"], "Tests of mutual or serial independence of random vectors with applications": ["Martin Bilodeau", "Aur\u00e9lien Guetsop Nangue"], "Recovering PCA and sparse PCA via hybrid- (l1, l2)sparse sampling of data elements": ["Abhisek Kundu", "Petros Drineas", "Malik Magdon-Ismail"], "Quantifying the informativeness of similarity measurements": ["Austin J. Brockmeier", "Tingting Mu", "Sophia Ananiadou", "John Y. Goulermas"], "Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis": ["Alessio Benavoli", "Giorgio Corani", "Janez Dem\u0161ar", "Marco Zaffalon"], "Relational reinforcement learning for planning with exogenous effects": ["David Mart\u00ednez", "Guillem Aleny\u00e0", "Tony Ribeiro", "Carme Torras"], "Bayesian tensor regression": ["Rajarshi Guhaniyogi", "Shaan Qamar", "David B. Dunson"], "Robust discriminative clustering with sparse regularizers": ["Nicolas Flammarion", "Balamurugan Palaniappan", "Francis Bach"], "Making decision trees feasible in ultrahigh feature and label dimensions": ["Weiwei Liu", "Ivor W. Tsang"], "Learning scalable deep kernels with recurrent structure": ["Maruan Al-Shedivat", "Andrew Gordon Wilson", "Yunus Saatchi", "Zhiting Hu", "Eric P. Xing"], "Convolutional neural networks analyzed via convolutional sparse coding": ["Vardan Papyan", "Yaniv Romano", "Michael Elad"], "Stochastic primal-dual coordinate method for regularized empirical risk minimization": ["Yuchen Zhang", "Lin Xiao"], "Angle-based multicategory distance-weighted SVM": ["Hui Sun", "Bruce A. Craig", "Lingsong Zhang"], "Minimax estimation of kernel mean embeddings": ["Ilya Tolstikhin", "Bharath K. Sriperumbudur", "Krikamol Muandet"], "The impact of random models on clustering similarity": ["Alexander J. Gates", "Yong-Yeol Ahn"], "Hierarchical clustering via spreading metrics": ["Aurko Roy", "Sebastian Pokutta"], "The MADP toolbox: an open source library for planning and learning in (multi-)agent systems": ["Frans A. Oliehoek", "Matthijs T. J. Spaan", "Bas Terwijn", "Philipp Robbel", "Jo\u00e3o V. Messias"], "A survey of algorithms and analysis for adaptive online learning": ["H. Brendan McMahan"], "A distributed block coordinate descent method for training l1regularized linear classifiers": ["Dhruv Mahajan", "S. Sathiya Keerthi", "S. Sundararajan"], "Distributed learning with regularized least squares": ["Shao-Bo Lin", "Xin Guo", "Ding-Xuan Zhou"], "Identifying unreliable and adversarial workers in crowdsourced labeling tasks": ["Srikanth Jagabathula", "Lakshminarayanan Subramanian", "Ashwin Venkataraman"], "An easy-to-hard learning paradigm for multiple classes and multiple labels": ["Weiwei Liu", "Ivor W. Tsang", "Klaus-Robert M\u00fcller"], "Fisher consistency for prior probability shift": ["Dirk Tasche"], "OpenXBOW: introducing the passau open-source crossmodal bag-of-words toolkit": ["Maximilian Schmitt", "Bj\u00f6rn Schuller"], "Optimal rates for multi-pass stochastic gradient methods": ["Junhong Lin", "Lorenzo Rosasco"], "Rank determination for low-rank data completion": ["Morteza Ashraphijuo", "Xiaodong Wang", "Vaneet Aggarwal"], "Bayesian network learning via topological order": ["Young Woong Park", "Diego Klabjan"], "Stability of controllers for Gaussian process dynamics": ["Julia Vinogradska", "Bastian Bischoff", "Duy Nguyen-Tuong", "Jan Peters"], "Harder, better, faster, stronger convergence rates for least-squares regression": ["Aymeric Dieuleveut", "Nicolas Flammarion", "Francis Bach"], "Confidence sets with expected sizes for multiclass classification": ["Christophe Denis", "Mohamed Hebiri"], "Online learning to rank with top-k feedback": ["Sougata Chaudhuri", "Ambuj Tewari"], "A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation": ["Thang D. Bui", "Josiah Yan", "Richard E. Turner"], "Accelerating stochastic composition optimization": ["Mengdi Wang", "Ji Liu", "Ethan X. Fang"], "Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server": ["Leonard Hasenclever", "Stefan Webb", "Thibaut Lienart", "Sebastian Vollmer", "Balaji Lakshminarayanan", "Charles Blundell", "Yee Whye Teh"], "Optimal dictionary for least squares representation": ["Mohammed Rayyan Sheriff", "Debasish Chatterjee"], "Computational limits of a distributed algorithm for smoothing spline": ["Zuofeng Shang", "Guang Cheng"], "Hinge-loss Markov random fields and probabilistic soft logic": ["Stephen H. Bach", "Matthias Broecheler", "Bert Huang", "Lise Getoor"], "Clustering with hidden Markov model on variable blocks": ["Lin Lin", "Jia Li"], "Approximation vector machines for large-scale online learning": ["Trung Le", "Tu Dinh Nguyen", "Vu Nguyen", "Dinh Phung"], "Efficient sampling from time-varying log-concave distributions": ["Hariharan Narayanan", "Alexander Rakhlin"], "Document neural autoregressive distribution estimation": ["Stanislas Lauly", "Yin Zheng", "Alexandre Allauzen", "Hugo Larochelle"], "Target curricula via selection of minimum feature sets: a case study in Boolean networks": ["Shannon Fenn", "Pablo Moscato"], "A general distributed dual coordinate optimization framework for regularized loss minimization": ["Shun Zheng", "Jialei Wang", "Fen Xia", "Wei Xu", "Tong Zhang"], "Second-order stochastic optimization for machine learning in linear time": ["Naman Agarwal", "Brian Bullins", "Elad Hazan"], "Regularized estimation and testing for high-dimensional multi-block vector-autoregressive models": ["Jiahe Lin", "George Michailidis"], "Learning theory of distributed regression with bias corrected regularization kernel network": ["Zheng-Chu Guo", "Lei Shi", "Qiang Wu"], "Probabilistic line searches for stochastic optimization": ["Maren Mahsereci", "Philipp Hennig"], "Learning instrumental variables with structural and non-gaussianity assumptions": ["Ricardo Silva", "Shohei Shimizu"], "Classification of time sequences using graphs of temporal constraints": ["Mathieu Guillame-Bert", "Artur Dubrawski"], "Distributed stochastic variance reduced gradient methods by sampling extra data with replacement": ["Jason D. Lee", "Qihang Lin", "Tengyu Ma", "Tianbao Yang"], "Kernel partial least squares for stationary data": ["Marco Singer", "Tatyana Krivobokova", "Axel Munk"], "Robust and scalable bayes via a median of subset posterior measures": ["Stanislav Minsker", "Sanvesh Srivastava", "Lizhen Lin", "David B. Dunson"], "Statistical and computational guarantees for the Baum-Welch algorithm": ["Fanny Yang", "Sivaraman Balakrishnan", "Martin J. Wainwright"], "Online but accurate inference for latent variable models with local Gibbs sampling": ["Christophe Dupuy", "Francis Bach"], "Poisson random fields for dynamic feature models": ["Valerio Perrone", "Paul A. Jenkins", "Dario Span\u00f2", "Yee Whye Teh"], "Gap safe screening rules for sparsity enforcing penalties": ["Eugene Ndiaye", "Olivier Fercoq", "Alexandre Gramfort", "Joseph Salmon"], "Minimax filter: learning to preserve privacy from inference attacks": ["Jihun Hamm"], "Knowledge graph completion via complex tensor factorization": ["Th\u00e9o Trouillon", "Christopher R. Dance", "\u00c9ric Gaussier", "Johannes Welbl", "Sebastian Riedel", "Guillaume Bouchard"], "Stabilized sparse online learning for sparse data": ["Yuting Ma", "Tian Zheng"], "Active-set methods for submodular minimization problems": ["K. S. Sesh Kumar", "Francis Bach"], "A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations": ["Jean-Baptiste Schiratti", "St\u00e9phanie Allassonni\u00e8re", "Olivier Colliot", "Stanley Durrleman"], "Stochastic gradient descent as approximate Bayesian inference": ["Stephan Mandt", "Matthew D. Hoffman", "David M. Blei"], "STORE: sparse tensor response regression and neuroimaging analysis": ["Will Wei Sun", "Lexin Li"], "A survey of preference-based reinforcement learning methods": ["Christian Wirth", "Riad Akrour", "Gerhard Neumann", "Johannes F\u00fcrnkranz"], "Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising": ["J\u00e9r\u00e9mie Bigot", "Charles Deledalle", "Delphine F\u00e9ral"], "Dimension estimation using random connection models": ["Paulo Serra", "Michel Mandjes"], "Bayesian inference for spatio-temporal spike-and-slab priors": ["Michael Riis Andersen", "Aki Vehtari", "Ole Winther", "Lars Kai Hansen"], "Adaptive randomized dimension reduction on massive data": ["Gregory Darnell", "Stoyan Georgiev", "Sayan Mukherjee", "Barbara E. Engelhardt"], "A nonconvex approach for phase retrieval: reshaped wirtinger flow and incremental algorithms": ["Huishuai Zhang", "Yi Zhou", "Yingbin Liang", "Yuejie Chi"], "Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering": ["Pietro Coretto", "Christian Hennig"], "On computationally tractable selection of experiments in measurement-constrained regression models": ["Yining Wang", "Adams Wei Yu", "Aarti Singh"], "Generalized conditional gradient for sparse estimation": ["Yaoliang Yu", "Xinhua Zhang", "Dale Schuurmans"], "Following the leader and fast rates in online linear prediction: curved constraint sets and other regularities": ["Ruitong Huang", "Tor Lattimore", "Andr\u00e1s Gy\u00f6rgy", "Csaba Szepesv\u00e1ri"], "Regularization and the small-ball method II: complexity dependent error rates": ["Guillaume Lecu\u00e9", "Shahar Mendelson"], "Matrix completion with noisy entries and outliers": ["Raymond K. W. Wong", "Thomas C. M. Lee"], "Faithfulness of probability distributions and graphs": ["Kayvan Sadeghi"], "Community extraction in multilayer networks with heterogeneous community structure": ["James D. Wilson", "John Palowitch", "Shankar Bhamidi", "Andrew B. Nobel"], "On binary embedding using circulant matrices": ["Felix X. Yu", "Aditya Bhaskara", "Sanjiv Kumar", "Yunchao Gong", "Shih-Fu Chang"], "Variational Fourier features for Gaussian processes": ["James Hensman", "Nicolas Durrande", "Arno Solin"], "HyperTools: a python toolbox for gaining geometric insights into high-dimensional data": ["Andrew C. Heusser", "Kirsten Ziman", "Lucy L. W. Owen", "Jeremy R. Manning"], "Automatic differentiation in machine learning: a survey": ["At\u0131l\u0131m G\u00fcnes Baydin", "Barak A. Pearlmutter", "Alexey Andreyevich Radul", "Jeffrey Mark Siskind"], "Normal bandits of unknown means and variances": ["Wesley Cowan", "Junya Honda", "Michael N. Katehakis"], "Cost-sensitive learning with noisy labels": ["Nagarajan Natarajan", "Inderjit S. Dhillon", "Pradeep Ravikumar", "Ambuj Tewari"], "Provably correct algorithms for matrix column subset selection with selectively sampled data": ["Yining Wang", "Aarti Singh"], "A study of the classification of low-dimensional data with supervised manifold learning": ["Elif Vural", "Christine Guillemot"], "Probabilistic preference learning with the mallows rank model": ["Valeria Vitelli", "\u00d8ystein S\u00f8rensen", "Marta Crispino", "Arnoldo Frigessi", "Elja Arjas"], "Robust topological inference: distance to a measure and kernel distance": ["Fr\u00e9d\u00e9ric Chazal", "Brittany Fasy", "Fabrizio Lecci", "Bertrand Michel", "Alessandro Rinaldo", "Alessandro Rinaldo"], "Training Gaussian mixture models at scale via coresets": ["Mario Lucic", "Matthew Faulkner", "Andreas Krause", "Dan Feldman"], "Gradient estimation with simultaneous perturbation and compressive sensing": ["Vivek S. Borkar", "Vikranth R. Dwaracherla", "Neeraja Sahasrabudhe"], "Principled selection of hyperparameters in the latent dirichlet allocation model": ["Clint P. George", "Hani Doss"], "Deep learning the ising model near criticality": ["Alan Morningstar", "Roger G. Melko"], "Pomegranate: fast and flexible probabilistic modeling in python": ["Jacob Schreiber"], "Maximum principle based algorithms for deep learning": ["Qianxiao Li", "Long Chen", "Cheng Tai", "E. Weinan"], "Gradient hard thresholding pursuit": ["Xiao-Tong Yuan", "Ping Li", "Tong Zhang"], "Risk-constrained reinforcement learning with percentile risk criteria": ["Yinlam Chow", "Mohammad Ghavamzadeh", "Lucas Janson", "Marco Pavone"], "Local Identifiability of \u21131-minimization dictionary learning: a sufficient and almost necessary condition": ["Siqi Wu", "Bin Yu"], "In search of coherence and consensus: measuring the interpretability of statistical topics": ["Fred Morstatter", "Huan Liu"], "On the behavior of intrinsically high-dimensional spaces: distances, direct and reverse nearest neighbors, and hubness": ["Fabrizio Angiulli"], "Convergence of unregularized online learning algorithms": ["Yunwen Lei", "Lei Shi", "Zheng-Chu Guo"], "Convergence analysis of distributed inference with vector-valued Gaussian belief propagation": ["Jian Du", "Shaodan Ma", "Yik-Chung Wu", "Soummya Kar", "Jos\u00e9 M. F. Moura"], "auDeep: unsupervised learning of representations from audio with deep recurrent neural networks": ["Michael Freitag", "Shahin Amiriparian", "Sergey Pugachevskiy", "Nicholas Cummins", "Bj\u00f6rn Schuller"], "On the stability of feature selection algorithms": ["Sarah Nogueira", "Konstantinos Sechidis", "Gavin Brown"], "Maximum likelihood estimation for mixtures of spherical Gaussians is NP-hard": ["Christopher Tosh", "Sanjoy Dasgupta"], "The DFS fused lasso: linear-time denoising over general graphs": ["Oscar Hernan Madrid Padilla", "James Sharpnack", "James G. Scott"], "Community detection and stochastic block models: recent developments": ["Emmanuel Abbe"], "On b-bit min-wise hashing for large-scale regression and classification with sparse data": ["Rajen D. Shah", "Nicolai Meinshausen"], "Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity": ["Quanming Yao", "James T. Kwok"], "Mode-seeking clustering and density ridge estimation via direct estimation of density-derivative-ratios": ["Hiroaki Sasaki", "Takafumi Kanamori", "Aapo Hyv\u00e4rinen", "Gang Niu", "Masashi Sugiyama"], "To tune or not to tune the number of trees in random forest": ["Philipp Probst", "Anne-Laure Boulesteix"], "Divide-and-conquer for debiased l1-norm support vector machine in ultra-high dimensions": ["Heng Lian", "Zengyan Fan"], "Beyond the hazard rate: more perturbation algorithms for adversarial multi-armed bandits": ["Zifan Li", "Ambuj Tewari"], "On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization": ["Xingguo Li", "Tuo Zhao", "Raman Arora", "Han Liu", "Mingyi Hong"], "Hyperband: a novel bandit-based approach to hyperparameter optimization": ["Lisha Li", "Kevin Jamieson", "Giulia DeSalvo", "Afshin Rostamizadeh", "Ameet Talwalkar"], "Submatrix localization via message passing": ["Bruce Hajek", "Yihong Wu", "Jiaming Xu"], "Quantized neural networks: training neural networks with low precision weights and activations": ["Itay Hubara", "Matthieu Courbariaux", "Daniel Soudry", "Ran El-Yaniv", "Yoshua Bengio"], "Significance-based community detection in weighted networks": ["John Palowitch", "Shankar Bhamidi", "Andrew B. Nobel"], "Kernel method for persistence diagrams via kernel embedding and weight factor": ["Genki Kusano", "Kenji Fukumizu", "Yasuaki Hiraoka"], "Pycobra: a python toolbox for ensemble learning and visualisation": ["Benjamin Guedj", "Bhargav Srinivasa Desikan"], "KeLP: a kernel-based learning platform": ["Simone Filice", "Giuseppe Castellucci", "Giovanni Da San Martino", "Alessandro Moschitti", "Danilo Croce", "Roberto Basili"], "Uncovering causality from multivariate hawkes integrated cumulants": ["Massil Achab", "Emmanuel Bacry", "St\u00e9phane Ga\u00efffas", "Iacopo Mastromatteo", "Jean-Fran\u00e7ois Muzy"], "Making better use of the crowd: how crowdsourcing can advance machine learning research": ["Jennifer Wortman Vaughan"], "Enhancing identification of causal effects by pruning": ["Santtu Tikka", "Juha Karvanen"], "Active nearest-neighbor learning in metric spaces": ["Aryeh Kontorovich", "Sivan Sabato", "Ruth Urner"], "From predictive methods to missing data imputation: an optimization approach": ["Dimitris Bertsimas", "Colin Pawlowski", "Ying Daisy Zhuo"], "Saturating splines and feature selection": ["Nicholas Boyd", "Trevor Hastie", "Stephen Boyd", "Benjamin Recht", "Michael I. Jordan"], "Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization": ["Andrei Patrascu", "Ion Necoara"], "Simple, robust and optimal ranking from pairwise comparisons": ["Nihar B. Shah", "Martin J. Wainwright"], "Surprising properties of dropout in deep networks": ["David P. Helmbold", "Philip M. Long"], "Exact learning of lightweight description logic ontologies": ["Boris Konev", "Carsten Lutz", "Ana Ozaki", "Frank Wolter"], "Sparse concordance-assisted learning for optimal treatment decision": ["Shuhan Liang", "Wenbin Lu", "Rui Song", "Lan Wang"], "Post-regularization inference for time-varying nonparanormal graphical models": ["Junwei Lu", "Mladen Kolar", "Han Liu"], "Permuted and augmented stick-breaking Bayesian multinomial regression": ["Quan Zhang", "Mingyuan Zhou"], "Steering social activity: a stochastic optimal control point of view": ["Ali Zarezade", "Abir De", "Utkarsh Upadhyay", "Hamid R. Rabiee", "Manuel Gomez-Rodriguez"], "The search problem in mixture models": ["Avik Ray", "Joe Neeman", "Sujay Sanghavi", "Sanjay Shakkottai"], "An \u2113\u221e eigenvector perturbation bound and its application to robust covariance estimation": ["Jianqing Fan", "Weichen Wang", "Yiqiao Zhong"], "A tight bound of hard thresholding": ["Jie Shen", "Ping Li"], "Estimation of graphical models through structured norm minimization": ["Davoud Ataee Tarzanagh", "George Michailidis"], "Sparse exchangeable graphs and their limits via graphon processes": ["Christian Borgs", "Jennifer T. Chayes", "Henry Cohn", "Nina Holden"], "Weighted SGD for \u2113p regression with randomized preconditioning": ["Jiyan Yang", "Yin-Lam Chow", "Christopher R\u00e9", "Michael W. Mahoney"], "Catalyst acceleration for first-order convex optimization: from theory to practice": ["Hongzhou Lin", "Julien Mairal", "Zaid Harchaoui"], "Gaussian lower bound for the information bottleneck limit": ["Amichai Painsky", "Naftali Tishby"], "Tick: a Python library for statistical learning, with an emphasis on hawkes processes and time-dependent models": ["Emmanuel Bacry", "Martin Bompaire", "Philip Deegan", "St\u00e9phane Ga\u00efffas", "S\u00f8ren V. Poulsen"], "SGDLibrary: a matlab library for stochastic optimization algorithms": ["Hiroyuki Kasai"], "Reward maximization under uncertainty: leveraging side-observations on networks": ["Swapna Buccapatnam", "Fang Liu", "Atilla Eryilmaz", "Ness B. Shroff"], "Simultaneous clustering and estimation of heterogeneous graphical models": ["Botao Hao", "Will Wei Sun", "Yufeng Liu", "Guang Cheng"], "Sketched ridge regression: optimization perspective, statistical perspective, and model averaging": ["Shusen Wang", "Alex Gittens", "Michael W. Mahoney"], "Compact convex projections": ["Steffen Gr\u00fcnew\u00e4lder"], "Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs": ["Emilija Perkovic", "Johannes Textor", "Markus Kalisch", "Marloes H. Maathuis"], "Katyusha: the first direct acceleration of stochastic gradient methods": ["Zeyuan Allen-Zhu"], "Average stability is invariant to data preconditioning: implications to exp-concave empirical risk minimization": ["Alon Gonen", "Shai Shalev-Shwartz"], "Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification": ["Prateek Jain", "Praneeth Netrapalli", "Sham M. Kakade", "Rahul Kidambi", "Aaron Sidford"], "Learning quadratic variance function (QVF) DAG models via overdispersion scoring (ODS)": ["Gunwoong Park", "Garvesh Raskutti"], "Improved spectral community detection in large heterogeneous networks": ["Hafiz Tiomoko Ali", "Romain Couillet"], "Statistical inference on random dot product graphs: a survey": ["Avanti Athreya", "Donniell E. Fishkind", "Minh Tang", "Carey E. Priebe", "Youngser Park", "Joshua T. Vogelstein", "Keith Levin", "Vince Lyzinski", "Yichen Qin"], "Rate of convergence of k-nearest-neighbor classification rule": ["Maik D\u00f6ring", "L\u00e1szl\u00f3 Gy\u00f6rfi", "Harro Walk"]}, {"Averaged collapsed variational bayes inference": ["averaged cvb", "collapsed variational bayes inference", "nonparametric bayes"], "Scalable inuence maximization for multiple products in continuous-time diffusion networks": ["continuous-time diffusion", "influence estimation", "influence maximization", "knapsack", "matroid", "model"], "Local algorithms for interactive clustering": [], "SnapVX: a network-based convex optimization solver": ["admm", "convex optimization", "data mining", "graphs", "network analytics"], "Communication-efficient sparse regression": ["averaging", "debiasing", "distributed sparse regression", "high-dimensional statistics", "lasso"], "Improving variational methods via pairwise linear response identities": ["graphical models", "linear response", "message passing algorithms", "statistical physics", "variational inference"], "Distributed sequence memory of multidimensional inputs in recurrent networks": ["low-rank recovery", "recurrent neural networks", "restricted isometry property", "short-term memory", "sparse signal recovery"], "Persistence images: a stable vector representation of persistent homology": ["dynamical systems", "machine learning", "persistence images", "persistent homology", "topological data analysis"], "Spectral clustering based on local PCA": ["intersecting clusters", "local principal component analysis", "multi-manifold clustering", "spectral clustering"], "On perturbed proximal gradient algorithms": ["monte carlo approximations", "perturbed majorization-minimization algorithms", "proximal gradient methods", "stochastic optimization"], "Differential privacy for bayesian inference through posterior sampling": ["adversarial learning", "bayesian inference", "differential privacy", "robustness"], "Refinery: an open source topic modeling web platform": ["software", "topic models", "visualization"], "Using conceptors to manage neural long-term memories for temporal patterns": ["neural dynamics", "neural long-term memory", "recurrent neural network", "temporal pattern learning"], "Automatic differentiation variational inference": ["approximate inference", "bayesian inference", "probabilistic programming"], "Empirical evaluation of resampling procedures for optimising SVM hyperparameters": ["bootstrap", "cross-validation", "hyperparameters", "k-fold", "resampling", "svm"], "A unified formulation and fast accelerated proximal gradient method for classification": ["binary classification", "minimum norm problem", "restarted accelerated proximal gradient method", "support vector machine", "vector projection computation"], "Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning": ["ensemble learning", "imbalanced dataset", "machine learning", "over-sampling", "python", "under-sampling"], "Information-geometric optimization algorithms: a unifying picture via invariance principles": ["black-box optimization", "evolution strategy", "information-geometric optimization", "invariance", "natural gradient", "randomized optimization", "stochastic optimization"], "Breaking the curse of dimensionality with convex neural networks": ["convex optimization", "convex relaxation", "neural networks", "non-parametric estimation"], "Memory efficient kernel approximation": ["kernel approximation", "kernel methods", "nystr\u00f6m method"], "On the equivalence between kernel quadrature rules and random feature expansions": ["integral operators", "positive-definite kernels", "quadrature"], "Analyzing tensor power method dynamics in overcomplete regime": ["latent variable models", "overcomplete representation", "tensor decomposition", "tensor power iteration", "unsupervised learning"], "JSAT: Java statistical analysis tool, a library for machine learning": ["java", "java library", "machine learning", "machine learning software", "open source"], "Identifying a minimal class of models for high-dimensional data": ["elastic net", "high-dimensional data", "lasso", "model selection", "simulated annealing"], "Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA": ["feature selection", "hyperparameter optimization", "model selection"], "POMDPs.jl: a framework for sequential decision making under uncertainty": ["julia", "mdp", "open-source", "pomdp", "sequential decision making"], "Generalized p\u00f3ya urn for time-varying pitman-yor processes": ["bayesian nonparametrics", "clustering", "dynamic models", "mixture models", "particle markov chain monte carlo", "sequential monte carlo"], "Particle gibbs split-merge sampling for Bayesian inference in mixture models": ["dirichlet process mixture models", "gibbs sampler", "particle gibbs sampler", "sequential monte carlo"], "Certifiably optimal low rank factor analysis": ["discrete optimization", "factor analysis", "first order methods", "global optimization", "nonlinear optimization", "rank minimization", "semidefinite optimization"], "Group sparse optimization via lp,q regularization": ["gene regulation network", "group sparse optimization", "iterative thresholding algorithm", "lower-order regularization", "nonconvex optimization", "proximal gradient method", "restricted eigenvalue condition"], "Preference-based teaching": ["learning half-spaces", "linear sets", "preference relation", "recursive teaching dimension", "teaching dimension"], "Nonparametric risk bounds for time-series forecasting": ["generalization error", "linear time-invariant systems", "model selection", "prediction risk", "statespace models", "vc dimension"], "Online Bayesian passive-aggressive learning": [], "Asymptotic analysis of objectives based on fisher information in active learning": ["asymptotic log-loss", "classification active learning", "fisher information ratio", "upper-bound minimization"], "A spectral algorithm for inference in hidden semi-Markov models": ["aviation safety", "graphical models", "hidden semi-markov model", "spectral algorithm", "tensor analysis"], "Simplifying probabilistic expressions in causal inference": ["causal inference", "graph theory", "graphical model", "probabilistic expression", "simplification"], "Nearly optimal classification for semimetrics": ["classification", "compression", "generalization", "semimetric"], "Bridging supervised learning and test-based co-optimization": ["active learning", "co-optimization", "free lunch", "optimal algorithms", "supervised learning"], "GFA: exploratory analysis of multiple data sources with group factor analysis": ["bayesian latent variable modelling", "biclustering", "data integration", "factor analysis", "multi-view learning"], "GPflow: a Gaussian process library using tensorflow": [], "COEVOLVE: a joint point process model for information diffusion and network evolution": ["co-evolutionary dynamics", "hawkes process", "information diffusion", "network structure", "point processes", "social networks", "survival analysis"], "Learning local dependence in ordered data": ["cholesky factor", "gaussian graphical models", "hierarchical group lasso", "local dependence", "precision matrices"], "Bayesian learning of dynamic multilayer networks": ["dynamic multilayer network", "edge prediction", "face-to-face contact network", "gaussian process", "latent space model"], "Time-accuracy tradeoffs in kernel prediction: controlling prediction quality": [], "Asymptotic behavior of support vector machine for spiked population model": ["asymptotic behavior", "spiked population model", "support vector machine"], "Distributed semi-supervised learning with kernel ridge regression": ["distributed learning", "error decomposition", "kernel ridge regression", "learning theory", "semi-supervised learning", "unlabeled data"], "On Markov chain Monte Carlo methods for tall data": [], "Explaining the success of adaboost and random forests as interpolating classifiers": ["adaboost", "classification", "overfitting", "random forests", "tree-ensembles"], "Clustering from general pairwise observations with applications to time-varying graphs": ["convex optimization", "dynamic graphs", "graph clustering", "information divergence", "low-rank matrix", "pairwise observation", "time-varying graphs"], "Uniform hypergraph partitioning: provable tensor methods and sampling techniques": ["hypergraph partitioning", "planted model", "sampling", "spectral method", "subspace clustering", "tensors"], "Reconstructing undirected graphs from eigenspaces": ["backward selection algorithm", "graphs", "identifiability", "stationary signal processing", "support recovery"], "An optimal algorithm for bandit and zero-order convex optimization with two-point feedback": ["bandit optimization", "gradient estimator", "stochastic optimization", "zero-order optimization"], "Perishability of data: dynamic pricing under varying-coefficient models": ["dynamic pricing", "hypothesis testing", "regret", "revenue management", "stochastic gradient descent", "varying-coefficient models"], "Two new approaches to compressed sensingexhibiting both robust sparse recovery and the grouping effect": ["compressed sensing", "elastic net", "lasso", "sparse group lasso", "sparse regression"], "On the consistency of ordinal regression methods": ["calibration", "excess risk bound", "fisher consistency", "ordinal regression", "surrogate loss"], "Statistical inference with unnormalized discrete models and localized homogeneous divergences": ["discrete model", "empirical localization", "homogeneous divergence", "unnormalized model"], "Density estimation in infinite dimensional exponential families": ["density estimation", "exponential family", "fisher divergence", "interpolation space", "inverse problem", "kernel density estimator", "maximum likelihood", "reproducing kernel hilbert space", "score matching", "tikhonov regularization"], "Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis": ["comparison-based algorithms", "k-relative neighborhood graph", "lens depth function", "non-metric multi-dimensional scaling", "ordinal data", "ordinal distance information", "ordinal embedding"], "Joint label inference in networks": ["graphs", "label inference", "label propagation", "social networks", "variational methods"], "Achieving optimal misclassification proportion in stochastic block models": ["clustering", "community detection", "minimax rates", "network analysis", "spectral clustering"], "On the propagation of low-rate measurement error to subgraph counts in large networks": ["limit distribution", "network analysis", "skellam distribution", "stein's method"], "Dense distributions from sparse samples: improved gibbs sampling parameter estimators for LDA": ["bayesian inference", "collapsed gibbs sampling", "cvb0", "latent dirichlet allocation", "multi-label classification", "text mining", "topic models", "unsupervised learning"], "Fundamental conditions for low-CP-rank tensor completion": ["algebraic geometry", "bernstein's theorem", "canonical polyadic decomposition", "finite completability", "low-rank tensor completion", "unique completability"], "Parallel symmetric class expression learning": ["description logic learning", "exception", "parallel", "symmetric"], "Learning partial policies to speedup MDP tree search via reduction to I.I.D. learning": ["imitation learning", "monte-carlo tree search", "online sequential decision-making", "partial policy", "partial policy learning", "reductions"], "Hierarchically compositional kernels for scalable nonparametric learning": ["hierarchical kernels", "nonparametric learning"], "Sharp oracle inequalities for square root regularization": ["karush-kuhn-tucker", "sharp oracale inequality", "square root lasso", "structured sparsity", "weak decomposability"], "Soft margin support vector classification as buffered probability minimization": ["binary classification", "buffered probability of exceedance", "conditional value-at-risk", "robust optimization", "support vector machines"], "Variational particle approximations": ["bayesian inference", "dirichlet process mixture model", "hidden markov model", "infinite relational model", "ising model", "variational methods"], "A Bayesian framework for learning rule sets for interpretable classification": ["association rules", "bayesian modeling", "data mining", "disjunctive normal form", "interpretable classifier", "statistical learning"], "A robust-equitable measure for feature ranking and selection": ["copula", "dependence measure", "equitability", "feature selection", "mutual information"], "Multiscale strategies for computing optimal transport": [], "Non-parametric policy search with limited information loss": ["kernel methods", "policy search", "reinforcement learning", "robotics"], "Tests of mutual or serial independence of random vectors with applications": ["distance covariance", "hilbert-schmidt independence criterion", "m\u00f6bius transformation", "mutual independence", "serial independence"], "Recovering PCA and sparse PCA via hybrid- (l1, l2)sparse sampling of data elements": ["element-wise sampling", "hybrid-", "pca", "sparse pca", "sparse representation"], "Quantifying the informativeness of similarity measurements": ["clustering", "correlation matrices", "information theory", "kernel methods", "quantum information theory", "similarity information"], "Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis": ["bayesian correlated t-test", "bayesian hierarchical correlated t-test", "bayesian hypothesis tests", "bayesian signed-rank test", "comparing classifiers", "null hypothesis significance testing", "pitfalls of p-values"], "Relational reinforcement learning for planning with exogenous effects": ["active learning", "learning models for planning", "model-based rl", "probabilistic planning", "robot learning"], "Bayesian tensor regression": ["magnetic resonance imaging", "multiway shrinkage prior", "parafac decomposition", "posterior consistency", "tensor regression"], "Robust discriminative clustering with sparse regularizers": [], "Making decision trees feasible in ultrahigh feature and label dimensions": ["classification", "perceptron decision tree", "ultrahigh feature dimensions", "ultrahigh label dimensions"], "Learning scalable deep kernels with recurrent structure": [], "Convolutional neural networks analyzed via convolutional sparse coding": ["basis pursuit", "convolutional neural networks", "convolutional sparse coding", "deep learning", "forward pass", "sparse representation", "thresholding algorithm"], "Stochastic primal-dual coordinate method for regularized empirical risk minimization": ["computational complexity", "convex-concave saddle", "empirical risk minimization", "point problems", "primal-dual algorithms", "randomized algorithms"], "Angle-based multicategory distance-weighted SVM": ["discriminant analysis", "distance-weighted discrimination", "high dimension", "imbalanced data", "support vector machine"], "Minimax estimation of kernel mean embeddings": ["bochner integral", "bochner's theorem", "kernel mean embeddings", "minimax lower bounds", "reproducing kernel hilbert space", "translation invariant kernel"], "The impact of random models on clustering similarity": ["adjustment for chance", "clustering comparison", "clustering evaluation", "normalized mutual information", "rand index"], "Hierarchical clustering via spreading metrics": ["approximation algorithms", "clustering", "convex optimization", "hierarchical clustering", "linear programming"], "The MADP toolbox: an open source library for planning and learning in (multi-)agent systems": ["decision-theoretic planning", "multiagent systems", "reinforcement learning", "software"], "A survey of algorithms and analysis for adaptive online learning": ["adaptive algorithms", "dual averaging", "follow-the-regularized-leader", "mirror descent", "online convex optimization", "online learning", "regret analysis"], "A distributed block coordinate descent method for training l1regularized linear classifiers": ["l1 regularization", "distributed learning"], "Distributed learning with regularized least squares": ["distributed learning", "divide-and-conquer", "error analysis", "integral operator", "second order decomposition"], "Identifying unreliable and adversarial workers in crowdsourced labeling tasks": ["adversary", "crowdsourcing", "outliers", "reputation"], "An easy-to-hard learning paradigm for multiple classes and multiple labels": ["classifier chain", "easy-to-hard learning paradigm", "multi-label classification", "multiclass classification"], "Fisher consistency for prior probability shift": ["class distribution estimation", "classification", "data set shift", "fisher consistency", "quantification"], "OpenXBOW: introducing the passau open-source crossmodal bag-of-words toolkit": ["bag-of-words", "feature learning", "histogram feature representations", "multimodal signal processing"], "Optimal rates for multi-pass stochastic gradient methods": [], "Rank determination for low-rank data completion": ["cp rank", "low-rank data completion", "manifold", "matrix", "multi-view matrix", "rank estimation", "tensor", "tensor-train rank", "tucker rank"], "Bayesian network learning via topological order": ["bayesian networks", "directed acyclic graphs", "gaussian bayesian network", "topological orders"], "Stability of controllers for Gaussian process dynamics": ["control", "gaussian process", "reinforcement learning", "stability"], "Harder, better, faster, stronger convergence rates for least-squares regression": ["accelerated gradient", "convex optimization", "least-squares regression", "non-parametric estimation", "stochastic gradient"], "Confidence sets with expected sizes for multiclass classification": ["confidence sets", "convex loss", "empirical risk minimization", "multiclass classification", "superlearning"], "Online learning to rank with top-k feedback": ["learning theory", "learning to rank", "online bandits", "online learning", "partial monitoring"], "A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation": ["expectation propagation", "gaussian process", "sparse approximation", "variational inference"], "Accelerating stochastic composition optimization": ["composition optimization", "large-scale optimization", "sample complexity", "stochastic gradient"], "Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server": ["bayesian learning", "deep learning", "distributed learning", "expectation propagation", "large scale learning", "markov chain monte carlo", "natural gradient", "parameter server", "posterior server", "stochastic approximation", "variational inference"], "Optimal dictionary for least squares representation": ["l2-optimal dictionary", "finite tight frames", "rank-1 decomposition"], "Computational limits of a distributed algorithm for smoothing spline": ["computational limits", "divide-and-conquer", "smoothing spline", "splitotic theory"], "Hinge-loss Markov random fields and probabilistic soft logic": ["probabilistic graphical models", "statistical relational learning", "structured prediction"], "Clustering with hidden Markov model on variable blocks": ["gaussian mixture model", "hidden markov model", "modal baum-welch algorithm", "modal clustering"], "Approximation vector machines for large-scale online learning": ["big data", "convergence analysis", "core set", "kernel", "large-scale machine learning", "online learning", "sparsity", "stochastic gradient descent"], "Efficient sampling from time-varying log-concave distributions": [], "Document neural autoregressive distribution estimation": ["autoregressive models", "deep learning", "language models", "neural networks", "topic models"], "Target curricula via selection of minimum feature sets: a case study in Boolean networks": ["boolean betworks", "k-feature set", "multi-label classification", "target curriculum"], "A general distributed dual coordinate optimization framework for regularized loss minimization": ["acceleration", "computational complexity", "distributed optimization", "regularized loss minimization", "stochastic dual coordinate ascent"], "Second-order stochastic optimization for machine learning in linear time": ["convex optimization", "regression", "second-order optimization"], "Regularized estimation and testing for high-dimensional multi-block vector-autoregressive models": ["block-coordinate descent", "consistency", "global testing", "stability", "vector-autoregression"], "Learning theory of distributed regression with bias corrected regularization kernel network": ["bias correction", "distributed learning", "error bound", "kernel method", "regularization"], "Probabilistic line searches for stochastic optimization": ["bayesian optimization", "gaussian processes", "learning rates", "line searches", "stochastic optimization"], "Learning instrumental variables with structural and non-gaussianity assumptions": ["causal discovery", "causality", "instrumental variables"], "Classification of time sequences using graphs of temporal constraints": ["classification", "decision forests", "graphical constraint models", "sequential data", "supervised learning", "symbolic and scalar time sequences", "temporal data"], "Distributed stochastic variance reduced gradient methods by sampling extra data with replacement": ["communication complexity", "distributed optimization", "first-order method", "lower bound", "stochastic variance reduced gradient"], "Kernel partial least squares for stationary data": ["effective dimensionality", "long range dependence", "nonparametric regression", "protein dynamics", "source condition"], "Robust and scalable bayes via a median of subset posterior measures": ["big data", "distributed computing", "geometric median", "parallel mcmc", "wasserstein distance"], "Statistical and computational guarantees for the Baum-Welch algorithm": ["baum-welch algorithm", "em algorithm", "graphical models", "hidden markov models", "non-convex optimization"], "Online but accurate inference for latent variable models with local Gibbs sampling": ["gibbs sampling", "latent dirichlet allocation", "latent variables models", "online learning", "topic modelling"], "Poisson random fields for dynamic feature models": ["bayesian nonparametrics", "indian buffet process", "markov chain monte carlo", "poisson random field", "topic model"], "Gap safe screening rules for sparsity enforcing penalties": ["convex optimization", "lasso", "multi-task lasso", "screening rules", "sparse logistic regression", "sparse-group lasso"], "Minimax filter: learning to preserve privacy from inference attacks": ["differential privacy", "empirical risk minimization", "inference attack", "k-anonymity", "minimax optimization"], "Knowledge graph completion via complex tensor factorization": ["complex embeddings", "knowledge graph", "matrix completion", "statistical relational learning", "tensor factorization"], "Stabilized sparse online learning for sparse data": ["adaptive shrinkage", "sparse features", "sparse online learning", "stability selection", "truncated gradient"], "Active-set methods for submodular minimization problems": ["convex optimization", "cut functions", "discrete optimization", "submodular function minimization", "total variation denoising"], "A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations": ["longitudinal model", "riemannian geometry", "spatiotemporal analysis", "stochastic expectation-maximization algorithm"], "Stochastic gradient descent as approximate Bayesian inference": ["approximate bayesian inference", "stochastic differential equations", "stochastic gradient mcmc", "stochastic optimization", "variational inference"], "STORE: sparse tensor response regression and neuroimaging analysis": ["functional connectivity analysis", "high-dimensional statistical learning", "magnetic resonance imaging", "non-asymptotic error bound", "tensor decomposition"], "A survey of preference-based reinforcement learning methods": ["markov decision process", "policy search", "preference learning", "preference-based reinforcement learning", "qualitative feedback", "reinforcement learning", "temporal difference learning"], "Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising": ["degrees of freedom", "exponential family", "gaussian spiked population model", "low-rank model", "matrix denoising", "optimal shrinkage rule", "random matrix theory", "singular value decomposition", "spectral estimator", "stein's unbiased risk estimate"], "Dimension estimation using random connection models": ["adaptation", "dimensionality reduction", "intrinsic dimension", "random connection model", "random graph"], "Bayesian inference for spatio-temporal spike-and-slab priors": ["bayesian inference", "expectation propagation", "linear inverse problems", "sparsity-promoting priors", "spike-and-slab priors"], "Adaptive randomized dimension reduction on massive data": ["dimension reduction", "generalized eigendecompositon", "genomics", "krylov subspace methods", "linear mixed models", "low-rank", "random projections", "randomized algorithms", "supervised"], "A nonconvex approach for phase retrieval: reshaped wirtinger flow and incremental algorithms": ["gradient descent", "nonconvex optimization", "phase retrieval", "regularity condition", "stochastic algorithms"], "Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering": ["ecm-algorithm", "improper density", "maximum likelihood", "mixture models", "model-based clustering", "robustness"], "On computationally tractable selection of experiments in measurement-constrained regression models": ["a-optimality", "computationally tractable methods", "minimax analysis", "optimal selection of experiments"], "Generalized conditional gradient for sparse estimation": ["dictionary learning", "frank-wolfe", "generalized conditional gradient", "matrix completion", "multi-view learning", "sparse estimation"], "Following the leader and fast rates in online linear prediction: curved constraint sets and other regularities": ["curvature", "follow the leader", "logarithmic regret", "online linear optimization", "strongly convex decision set"], "Regularization and the small-ball method II: complexity dependent error rates": ["empirical processes theory", "high-dimensional statistics", "learning theory", "minimax rates", "regularization"], "Matrix completion with noisy entries and outliers": ["es-algorithm", "huber function", "robust methods", "soft-impute", "stable recovery"], "Faithfulness of probability distributions and graphs": ["causal discovery", "compositional graphoid", "directed acyclic graph", "faithfulness", "graphical model selection", "independence model", "markov property", "mixed graph", "structural learning"], "Community extraction in multilayer networks with heterogeneous community structure": ["clustering", "community detection", "modularity", "multiplex networks", "score based methods"], "On binary embedding using circulant matrices": ["binary embedding", "circulant matrix", "dimensionality reduction", "fft", "structured matrix"], "Variational Fourier features for Gaussian processes": ["fourier features", "gaussian processes", "variational inference"], "HyperTools: a python toolbox for gaining geometric insights into high-dimensional data": ["dimensionality reduction", "high-dimensional", "procrustes", "time-series data", "visualization"], "Automatic differentiation in machine learning: a survey": ["backpropagation", "differentiable programming"], "Normal bandits of unknown means and variances": ["inflated sample means", "multi-armed bandits", "sequential allocation", "ucb policies"], "Cost-sensitive learning with noisy labels": ["class-conditional label noise", "cost-sensitive learning", "statistical consistency"], "Provably correct algorithms for matrix column subset selection with selectively sampled data": ["active learning", "column subset selection", "leverage scores"], "A study of the classification of low-dimensional data with supervised manifold learning": ["classification", "dimensionality reduction", "manifold learning", "out-of-sample extensions", "rbf interpolation"], "Probabilistic preference learning with the mallows rank model": ["incomplete rankings", "markov chain monte carlo", "pairwise comparisons", "preference learning with uncertainty", "recommendation systems"], "Robust topological inference: distance to a measure and kernel distance": ["persistent homology", "rkhs", "topological data analysis"], "Training Gaussian mixture models at scale via coresets": ["coresets", "gaussian mixture models", "streaming and distributed computation"], "Gradient estimation with simultaneous perturbation and compressive sensing": ["compressive sensing", "gradient descent", "gradient estimation", "gradient outer product matrix", "sparsity"], "Principled selection of hyperparameters in the latent dirichlet allocation model": ["empirical bayes inference", "latent dirichlet allocation", "markov chain monte carlo", "model selection", "topic modelling"], "Deep learning the ising model near criticality": ["deep belief network", "deep boltzmann machine", "deep learning", "restricted boltzmann machine"], "Pomegranate: fast and flexible probabilistic modeling in python": ["big data", "cython", "machine learning", "probabilistic modeling", "python"], "Maximum principle based algorithms for deep learning": ["deep learning", "method of successive approximations", "optimal control", "pontryagin's maximum principle"], "Gradient hard thresholding pursuit": ["greedy selection", "hard thresholding pursuit", "sparsity recovery"], "Risk-constrained reinforcement learning with percentile risk criteria": ["actor-critic algorithms", "chance-constrained optimization", "conditional value-at-risk", "markov decision process", "policy gradient algorithms", "reinforcement learning"], "Local Identifiability of \u21131-minimization dictionary learning: a sufficient and almost necessary condition": ["\u21131-minimization", "dictionary learning", "local minimum", "non-convex optimization", "sparse decomposition"], "In search of coherence and consensus: measuring the interpretability of statistical topics": [], "On the behavior of intrinsically high-dimensional spaces: distances, direct and reverse nearest neighbors, and hubness": ["distance concentration", "distribution of distances", "high-dimensional data", "hubness", "nearest neighbors", "reverse nearest neighbors"], "Convergence of unregularized online learning algorithms": ["convergence analysis", "learning theory", "online learning", "reproducing kernel hilbert space"], "Convergence analysis of distributed inference with vector-valued Gaussian belief propagation": ["graphical model", "large-scale networks", "linear gaussian model", "markov random field", "walk-summability"], "auDeep: unsupervised learning of representations from audio with deep recurrent neural networks": ["audio processing", "autoencoders", "deep feature learning", "recurrent neural networks", "sequence to sequence learning"], "On the stability of feature selection algorithms": ["feature selection", "stability"], "Maximum likelihood estimation for mixtures of spherical Gaussians is NP-hard": ["maximum likelihood", "mixtures of gaussians", "np-completeness"], "The DFS fused lasso: linear-time denoising over general graphs": ["depth-first search", "fused lasso", "graph denoising", "total variation denoising"], "Community detection and stochastic block models: recent developments": ["clustering", "community detection", "computational gaps", "network data analysis", "random graphs", "spectral algorithms", "stochastic block models", "unsupervised learning"], "On b-bit min-wise hashing for large-scale regression and classification with sparse data": ["large-scale data", "min-wise hashing", "resemblance kernel", "ridge regression", "sparse data"], "Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity": ["frank-wolfe algorithm", "matrix completion", "nonconvex optimization", "nonconvex regularization", "proximal algorithm"], "Mode-seeking clustering and density ridge estimation via direct estimation of density-derivative-ratios": ["density derivative", "density ridge estimation", "geometric feature", "mode-seeking clustering"], "To tune or not to tune the number of trees in random forest": ["bagging", "error rate", "number of trees", "out-of-bag", "random forest"], "Divide-and-conquer for debiased l1-norm support vector machine in ultra-high dimensions": ["classification", "debiased estimator", "distributed estimator", "divide and conquer", "sparsity"], "Beyond the hazard rate: more perturbation algorithms for adversarial multi-armed bandits": ["follow the perturbed leader", "gradient based algorithms", "multi-armed bandits", "online learning", "regret"], "On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization": ["cyclic block coordinate descent", "gradient descent", "improved iteration complexity", "quadratic minimization", "strongly convex minimization"], "Hyperband: a novel bandit-based approach to hyperparameter optimization": ["deep learning", "hyperparameter optimization", "infinite-armed bandits", "model selection", "online optimization"], "Submatrix localization via message passing": ["biclustering", "high-dimensional statistics", "message passing", "spectral algorithms computational complexity", "submatrix localization"], "Quantized neural networks: training neural networks with low precision weights and activations": ["computer vision", "deep learning", "energy efficient neural networks", "language models", "neural networks compression"], "Significance-based community detection in weighted networks": ["community detection", "multiple testing", "network models", "unsupervised learning", "weighted networks"], "Kernel method for persistence diagrams via kernel embedding and weight factor": ["kernel embedding", "kernel method", "persistence diagrams", "persistence weighted gaussian kernel", "topological data analysis"], "Pycobra: a python toolbox for ensemble learning and visualisation": ["ensemble methods", "machine learning", "open source software", "python", "voronoi tesselation"], "KeLP: a kernel-based learning platform": ["java framework", "kernel machines", "structured data and kernels"], "Uncovering causality from multivariate hawkes integrated cumulants": ["causality inference", "cumulants", "generalized method of moments", "hawkes process"], "Making better use of the crowd: how crowdsourcing can advance machine learning research": ["behavioral experiments", "crowdsourcing", "data generation", "hybrid intelligence", "incentives", "mechanical turk", "model evaluation"], "Enhancing identification of causal effects by pruning": ["algorithm", "causal inference", "causal model", "identifiability", "pruning"], "Active nearest-neighbor learning in metric spaces": ["active learning", "metric spaces", "nearest-neighbors", "non-parametric learning"], "From predictive methods to missing data imputation: an optimization approach": ["k-nn", "missing data imputation", "optimal decision trees", "svm"], "Saturating splines and feature selection": ["convex optimization", "feature selection", "lasso", "regression", "splines"], "Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization": ["intersection of convex constraints", "nonasymptotic convergence analysis", "rates of convergence", "stochastic convex optimization", "stochastic proximal point"], "Simple, robust and optimal ranking from pairwise comparisons": ["approximate recovery", "borda count", "occam's razor", "pairwise comparisons", "permutation-based models", "ranking", "set recovery"], "Surprising properties of dropout in deep networks": ["deep neural networks", "dropout", "learning theory", "regularization"], "Exact learning of lightweight description logic ontologies": ["complexity", "description logic", "exact learning"], "Sparse concordance-assisted learning for optimal treatment decision": ["concordance-assisted learning", "l1 norm", "optimal treatment regime", "support vector machine", "variable selection"], "Post-regularization inference for time-varying nonparanormal graphical models": ["graphical model selection", "hypothesis test", "nonparanormal graph", "regularized rank-based estimator", "time-varying network analysis"], "Permuted and augmented stick-breaking Bayesian multinomial regression": ["discrete choice models", "logistic regression", "nonlinear classification", "softplus regression", "support vector machines"], "Steering social activity: a stochastic optimal control point of view": ["information networks", "marked temporal point processes", "social networks", "stochastic differential equations with jumps", "stochastic optimal control"], "The search problem in mixture models": ["method of moments", "mixture models", "search", "semi-supervised", "side information"], "An \u2113\u221e eigenvector perturbation bound and its application to robust covariance estimation": ["approximate factor model", "incoherence", "low-rank matrices", "matrix perturbation theory", "sparsity"], "A tight bound of hard thresholding": ["compressed sensing", "hard thresholding", "sparsity", "stochastic optimization"], "Estimation of graphical models through structured norm minimization": ["alternating direction method of multipliers", "convergence", "gaussian covariance graph model", "markov rrandom fields", "regularization", "structured sparse norm"], "Sparse exchangeable graphs and their limits via graphon processes": ["exchangeable graph models", "graph convergence", "graphons", "modelling of sparse networks", "sparse graph convergence"], "Weighted SGD for \u2113p regression with randomized preconditioning": [], "Catalyst acceleration for first-order convex optimization: from theory to practice": ["convex optimization", "first-order methods", "large-scale machine learning"], "Gaussian lower bound for the information bottleneck limit": ["ace", "canonical correlations", "gaussianization", "infomax", "information bottleneck", "mutual information maximization"], "Tick: a Python library for statistical learning, with an emphasis on hawkes processes and time-dependent models": ["generalized linear models", "hawkes processes", "optimization", "point process", "python", "statistical learning", "survival analysis"], "SGDLibrary: a matlab library for stochastic optimization algorithms": ["finite-sum minimization problem", "large-scale optimization problem", "stochastic gradient", "stochastic optimization"], "Reward maximization under uncertainty: leveraging side-observations on networks": ["bipartite graph", "multi-armed bandits", "regret bounds", "side observations"], "Simultaneous clustering and estimation of heterogeneous graphical models": ["clustering", "finite-sample analysis", "graphical models", "high-dimensional statistics", "non-convex optimization"], "Sketched ridge regression: optimization perspective, statistical perspective, and model averaging": ["matrix sketching", "randomized linear algebra", "ridge regression"], "Compact convex projections": [], "Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs": ["causal effects", "confounding", "covariate adjustment", "graphical models", "latent variables"], "Katyusha: the first direct acceleration of stochastic gradient methods": [], "Average stability is invariant to data preconditioning: implications to exp-concave empirical risk minimization": [], "Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification": ["agnostic learning", "batchsize doubling", "heteroscedastic noise", "iterate averaging", "least squares regression", "mini batch sgd", "mis-specified models", "model averaging", "parallelization", "parameter mixing", "stochastic approximation", "stochastic gradient descent", "suffix averaging"], "Learning quadratic variance function (QVF) DAG models via overdispersion scoring (ODS)": ["bayesian networks", "directed acyclic graph", "identifiability", "multi-variate count distribution", "overdispersion"], "Improved spectral community detection in large heterogeneous networks": ["community detection", "heterogeneous graphs", "random matrix theory", "random networks", "spectral clustering"], "Statistical inference on random dot product graphs: a survey": ["adjacency spectral embedding", "laplacian spectral embedding", "multi-sample graph hypothesis testing", "random dot product graph", "semiparametric modeling"], "Rate of convergence of k-nearest-neighbor classification rule": ["classification", "error probability", "k-nearest-neighbor rule", "rate of convergence"]}, {"Katsuhiko Ishiguro": "Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan", "Issei Sato": "NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan", "Nan Du": "Department of Computer Science, Princeton University, Princeton, NJ", "Yingyu Liang": "School of Computer Science, Carnegie Mellon University, Pittsburgh, PA", "Maria-Florina Balcan": "MPI for Software Systems, Kaiserslautern, Germany", "Hongyuan Zha": "College of Computing, Georgia Institute of Technology, Atlanta, GA", "Pranjal Awasthi": "School of Computer Science, Carnegie Mellon University", "Maria Florina Balcan": "Google, NY", "David Hallac": "Department of Computer Science, Stanford University, Stanford, CA", "Christopher Wong": "Department of Computer Science, Stanford University, Stanford, CA", "Steven Diamond": "Department of Computer Science, Stanford University, Stanford, CA", "Abhijit Sharang": "Department of Computer Science, Stanford University, Stanford, CA", "Rok Sosic": "Department of Electrical Engineering, Stanford University, Stanford, CA", "Stephen Boyd": "Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA", "Jason D. Lee": "Tippie College of Business, University of Iowa Iowa City, IA", "Qiang Liu": "Department of Statistics, University of Michigan, Ann Arbor, MI", "Yuekai Sun": "Department of Statistics, Stanford University, Stanford, CA", "Jack Raymond": "Dipartimento di Fisica, INFN-Sezione di Roma1 and CNR-Nanotec, La Sapienza University of Rome, Rome, Italy", "Adam S. Charles": "Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA", "Dong Yin": "School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA", "Henry Adams": "Department of Mathematics, Colorado State University, Fort Collins, CO", "Tegan Emerson": "Department of Mathematics, Colorado State University, Fort Collins, CO", "Michael Kirby": "Department of Mathematics, Colorado State University, Fort Collins, CO", "Rachel Neville": "Department of Mathematics, Colorado State University, Fort Collins, CO", "Chris Peterson": "Department of Mathematics, Colorado State University, Fort Collins, CO", "Patrick Shipman": "Department of Mathematics and Computer Science, Wilkes University, Wilkes-Barre, PA", "Sofya Chepushtanova": "Department of Mathematics, Texas Christian University, Fort Worth, TX", "Eric Hanson": "Department of Mathematics, Duke University, Durham, NC", "Francis Motta": "Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, MN", "Ery Arias-Castro": "Department of Mathematics, University of Minnesota, Twin Cities, Minneapolis, MN", "Gilad Lerman": "Department of Mathematics, University of Central Florida, Orlando, FL", "Yves F. Atchad\u00e9": "LTCI, CNRS, Telecom ParisTech, Universit\u00e9 Paris-Saclay, Paris, France", "Gersende Fort": "CMAP, INRIA XPOP, Ecole Polytechnique, Palaiseau, France", "Christos Dimitrakakis": "Google, Inc., Mountain View, CA", "Blaine Nelson": "School of Mathematics & Statistics, The University of Melbourne, Parkville, VIC, Australia", "Zuhe Zhang": "Department of Computer Science & Engineering, Chalmers University of Technology, Gothenburg, Sweden", "Aikaterini Mitrokotsa": "School of Computing & Information Systems, The University of Melbourne, Parkville, VIC, Australia", "Daeil Kim": "Department of Computer Science, Brown University, Providence, RI", "Benjamin F. Swanson": "Department of Computer Science, Brown University, Providence, RI", "Michael C. Hughes": "Department of Computer Science, Brown University, Providence, RI", "Alp Kucukelbir": "Department of Computer Science, Columbia University, New York, NY", "Dustin Tran": "Department of Computer Science, Princeton University, Princeton, NJ", "Rajesh Ranganath": "Data Science Institute, Departments of Political Science and Statistics, Columbia University, New York, NY", "Andrew Gelman": "Data Science Institute, Departments of Computer Science and Statistics, Columbia University, New York, NY", "Jacques Wainer": "School of Computing Sciences, University of East Anglia, Norwich, UK", "Naoki Ito": "Department of Mathematical Analysis and Statistical Inference, The Institute of Statistical Mathematics, Tokyo, Japan", "Akiko Takeda": "Department of Mathematics, National University of Singapore, Singapore", "Guillaume Lema\u00eetre": "ShoppeAI, Toronto, Ontario, Canada", "Yann Ollivier": "Univ. Paris-Sud, LRI, Orsay, France", "Ludovic Arnold": "Inria & CMAP, Ecole polytechnique, Palaiseau, France", "Anne Auger": "Inria & CMAP, Ecole polytechnique, Palaiseau, France", "Si Si": "Departments of Computer Science and Statistics, University of California, Davis, Davis, CA", "Cho-Jui Hsieh": "Department of Computer Science, University of Texas at Austin, Austin, TX", "Animashree Anandkumar": "Department of Computer Science, Duke University, Durham, NC", "Rong Ge": "Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA", "Daniel Nevo": "Department of Statistics, The Hebrew University of Jerusalem, Jerusalem, Israel and Department of Statistics, University of Michigan, Ann Arbor, MI", "Lars Kotthoff": "Department of Computer Science, University of British Columbia, Vancouver, B.C., Canada", "Chris Thornton": "Department of Computer Science, University of British Columbia, Vancouver, B.C., Canada", "Holger H. Hoos": "Department of Computer Science, University of British Columbia, Vancouver, B.C., Canada", "Frank Hutter": "Department of Computer Science, University of British Columbia, Vancouver, B.C., Canada", "Maxim Egorov": "Department of Aeronautics and Astronautics, Stanford University, Stanford, CA", "Zachary N. Sunberg": "Department of Aeronautics and Astronautics, Stanford University, Stanford, CA", "Edward Balaban": "Department of Aeronautics and Astronautics, Stanford University, Stanford, CA", "Tim A. Wheeler": "Department of Aeronautics and Astronautics, Stanford University, Stanford, CA", "Jayesh K. Gupta": "Department of Aeronautics and Astronautics, Stanford University, Stanford, CA", "Fran\u00e7ois Caron": "Machine Learning Department, Carnegie Mellon University, Pittsburgh", "Willie Neiswanger": "Department of Engineering Science, University of Oxford, Oxford, UK", "Frank Wood": "Department of Statistics, University of Oxford, Oxford, UK", "Arnaud Doucet": "Department of Statistics, University of Oxford, Oxford, United Kingdom", "Alexandre Bouchard-C\u00f4t\u00e9": "Department of Statistics, University of Oxford, United Kingdom", "Dimitris Bertsimas": "Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA", "Martin S. Copenhaver": "Sloan School of Management, MIT, Cambridge, MA", "Yaohua Hu": "School of Mathematical Sciences, Zhejiang University, Hangzhou, P. R. China", "Chong Li": "School of Economics and Management, Southwest Jiaotong University, Chengdu, P. R. China", "Kaiwen Meng": "School of Life Sciences, The Chinese University of Hong Kong and Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, P. R. China", "Jing Qin": "Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong", "Ziyuan Gao": "Department of Mathematics, Ruhr-University Bochum", "Christoph Ries": "Department of Mathematics, Ruhr-University Bochum", "Hans U. Simon": "Department of Computer Science, University of Regina", "Daniel J. McDonald": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Cosma Rohilla Shalizi": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Tianlin Shi": "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 ...", "Jamshid Sourati": "Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA", "Murat Akcakaya": "Georgetown University, Washington D.C.", "Todd K. Leen": "Department of Electrical and Computer Engineering, Northeastern University, Boston, MA", "Deniz Erdogmus": "Department of Electrical and Computer Engineering, Northeastern University, Boston, MA", "Igor Melnyk": "Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN", "Santtu Tikka": "Department of Mathematics and Statistics, University of Jyvaskyla, Finland", "Lee-Ad Gottlieb": "Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel", "Aryeh Kontorovich": "Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israel", "Eemeli Lepp\u00e4aho": "Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Muhammad Ammad-ud-din": "Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Aalto, Finland", "Alexander G. De G. Matthews": "Department of Engineering, University of Cambridge, Cambridge, UK", "Mark Van Der Wilk": "Department of Engineering Science, University of Oxford, Oxford, UK", "Tom Nickson": "Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University, Japan", "Keisuke Fujii": "Manchester University, Manchester, UK", "Alexis Boukouvalas": "School of Informatics, University of Edinburgh, Edinburgh, UK", "Pablo Le\u00f3n-Villagr\u00e1": "Department of Engineering, University of Cambridge, Cambridge, UK", "Zoubin Ghahramani": "CHICAS, Faculty of Health and Medicine, Lancaster University, Lancaster, UK", "Mehrdad Farajtabar": "College of Computing, Georgia Institute of Technology, Atlanta, GA", "Yichen Wang": "MPI for Software Systems, Kaiserslautern, Germany", "Shuang Li": "College of Computing, Georgia Institute of Technology, Atlanta, GA", "Guo Yu": "Department of Biological Statistics and Computational Biology and Department of Statistical Science, Cornell University, Ithaca, NY", "Daniele Durante": "Department of Statistical Science, Duke University, Durham, NC", "Nabanita Mukherjee": "Departments of Statistical Science and Computer Science, Duke University, Durham, NC", "Samory Kpotufe": "HHMI", "Xiangyu Chang": "Department of Statistics, Wenzhou University, Wenzhou, China", "Shao-Bo Lin": "Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong", "R\u00e9mi Bardenet": "Department of Statistics, University of Oxford, Oxford, United Kingdom", "Abraham J. Wyner": "Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA", "Matthew Olson": "Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA", "Justin Bleich": "Apple Inc.", "Shiau Hong Lim": "School of Operations Research and Information Engineering, Cornell University, Ithaca, NY", "Yudong Chen": "School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA", "Debarghya Ghoshdastidar": "Department of Computer Science & Automation, Indian Institute of Science, Bangalore, India", "Yohann De Castro": "Institut Camille Jordan, Universit\u00e9 Claude Bernard Lyon 1, Villeurbanne, France", "Thibault Espinasse": "Laboratoire de Math\u00e9matiques Jean Leray, Universit\u00e9 de Nantes, Nantes, France", "Mehmet Eren Ahsen": "Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX", "Niharika Challapalli": "Department of Systems Engineering, University of Texas at Dallas, Richardson, TX and Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India", "Fabian Pedregosa": "D\u00e9partement d'informatique de l'ENS, \u00c9cole normale sup\u00e9rieure, CNRS, PSL Research University, Paris, France", "Francis Bach": "LTCI, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay INRIA, Universit\u00e9 Paris-Saclay, Saclay, France", "Takashi Takenouchi": "Department of Computing and Software Systems, Nagoya University, Nagoya, Japan", "Bharath Sriperumbudur": "The Institute of Statistical Mathematics, Tokyo, Japan", "Kenji Fukumizu": "Advanced Institute for Materials Research, Tohoku University, Sendai, Miyagi, Japan", "Arthur Gretton": "University College London, London, UK", "Aapo Hyv\u00e4rinen": "Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan and Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan", "Matth\u00e4us Kleindessner": "Department of Computer Science, University of T\u00fcbingen, T\u00fcbingen, Germany", "Deepayan Chakrabarti": "Cerebras Systems", "Chao Gao": "University of Pennsylvania", "Zongming Ma": "Yale University", "Anderson Y. Zhang": "Yale University", "Prakash Balachandran": "Department of Mathematics & Statistics, Boston University, Boston, MA", "Eric D. Kolaczyk": "Department of Biomedical Data Science, Dartmouth College, Hanover, NH", "Yannis Papanikolaou": "California Institute for Telecommunications and Information Technology, University of California, San Diego, CA", "James R. Foulds": "SurveyMonkey, San Mateo, CA", "Morteza Ashraphijuo": "Columbia University, New York, NY", "An C. Tran": "School of Engineering and Advanced Technology, Massey University, New Zealand", "Jens Dietrich": "School of Engineering and Advanced Technology, Massey University, New Zealand", "Hans W. Guesgen": "School of Engineering and Advanced Technology, Massey University, New Zealand", "Jervis Pinto": "School of EECS, Oregon State University, Corvallis, OR", "Jie Chen": "Tel Aviv University", "Haim Avron": "Google Brain, NYC", "Benjamin Stucky": "Seminar for Statistics, ETH Z\u00fcrich, Zurich, Switzerland", "Matthew Norton": "Risk Management and Financial Engineering Lab, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL", "Alexander Mafusalov": "Risk Management and Financial Engineering Lab, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL", "Ardavan Saeedi": "DeepMind, London", "Vikash K. Mansinghka": "Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA", "Tong Wang": "Duke University", "Cynthia Rudin": "Harvard University", "Finale Doshi-Velez": "Edward Jones", "Erica Klampfl": "Ford Motor Company", "A. Adam Ding": "Department of Electrical and Computer Engineering, Northeastern University, Boston, MA", "Jennifer G. Dy": "Department of Mathematics, Northeastern University, Boston, MA", "Yi Li": "Department of Electrical and Computer Engineering, Northeastern University, Boston, MA", "Herke Van Hoof": "Lincoln Centre for Autonomous Systems, Lincoln University, Lincoln, United Kingdom", "Gerhard Neumann": "Knowledge Engineering Group, Technische Universit\u00e4t Darmstadt, Darmstadt, Germany", "Martin Bilodeau": "D\u00e9partement de math\u00e9matiques et de statistique, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, Canada", "Abhisek Kundu": "Computer Science, Purdue University, West Lafayette, IN", "Petros Drineas": "Computer Science, Rensselaer Polytechnic Institute, Troy, NY", "Austin J. Brockmeier": "School of Computer Science, University of Manchester, Manchester, UK", "Tingting Mu": "School of Computer Science, University of Manchester, Manchester, UK", "Sophia Ananiadou": "Department of Computer Science, University of Liverpool, Liverpool, UK", "Alessio Benavoli": "Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno, Switzerland", "Giorgio Corani": "Istituto Dalle Molle di Studi sull'Intelligenza Artificiale Galleria, Manno, Switzerland", "Janez Dem\u0161ar": "Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno, Switzerland", "David Mart\u00ednez": "Institut de Rob\u00f2tica i Inform\u00e0tica Industrial, Barcelona, Spain", "Guillem Aleny\u00e0": "Laboratoire des sciences du num\u00e9rique de Nante, Nantes, France", "Rajarshi Guhaniyogi": "Google Inc., Mountain View, CA", "Shaan Qamar": "Department of Statistical Science, Duke University, Durham, NC", "Nicolas Flammarion": "INRIA, D\u00e9partement d'Informatique de l\u2019ENS, Ecole Normale Sup\u00e9rieure, CNRS, PSL Research University, Paris, France", "Balamurugan Palaniappan": "INRIA, D\u00e9partement d'Informatique de l\u2019ENS, Ecole Normale Sup\u00e9rieure, CNRS, PSL Research University, Paris, France", "Weiwei Liu": "Centre for Artificial Intelligence, FEIT, University of Technology Sydney, NSW, Australia", "Maruan Al-Shedivat": "Cornell University", "Andrew Gordon Wilson": "Carnegie Mellon University", "Yunus Saatchi": "Carnegie Mellon University", "Zhiting Hu": "Carnegie Mellon University", "Vardan Papyan": "Department of Computer Science, Technion - Israel Institute of Technology, Technion City, Haifa, Israel", "Yaniv Romano": "Department of Computer Science, Technion - Israel Institute of Technology, Technion City, Haifa, Israel", "Yuchen Zhang": "Microsoft Research, Redmond, WA", "Hui Sun": "Department of Statistics, Purdue University West Lafayette, IN", "Bruce A. Craig": "Department of Statistics, Purdue University West Lafayette, IN", "Ilya Tolstikhin": "Department of Statistics, Pennsylvania State University, University Park, PA", "Bharath K. Sriperumbudur": "Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, Thailand", "Alexander J. Gates": "Department of Informatics and Program in Cognitive Science, Indiana University, Bloomington, IN", "Aurko Roy": "ISyE, Georgia Institute of Technology, Atlanta, GA", "Frans A. Oliehoek": "Delft University of Technology, Delft, The Netherlands", "Matthijs T. J. Spaan": "Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands", "Bas Terwijn": "Media Lab, Massachusetts Institute of Technology Cambridge, MA", "Philipp Robbel": "Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands", "Dhruv Mahajan": "Office Data Science Group, Microsoft, Mountain View, CA", "S. Sathiya Keerthi": "Microsoft Research, Bangalore, India", "Xin Guo": "Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong", "Srikanth Jagabathula": "Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, NY", "Lakshminarayanan Subramanian": "Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, NY", "Ivor W. Tsang": "Machine Learning Group, Computer Science Berlin Institute of Technology, Berlin, Germany and Max Planck Institute for Informatics, Saarbrcken and Department of Brain and Cognitive Engineering Kore ...", "Maximilian Schmitt": "Chair of Complex and Intelligent Systems, University of Passau, Passau, Germany", "Junhong Lin": "DIBRIS, Universit\u00e0 di Genova, Genova, Italy and Laboratory for Computational and Statistical Learning, Istituto Italiano di Tecnologia and Massachusetts Institute of Technology, Cambridge, MA", "Xiaodong Wang": "Purdue University, West Lafayette, IN", "Young Woong Park": "Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL", "Julia Vinogradska": "Intel ligent Autonomous Systems Lab, Technische Universit\u00e4t Darmstadt, Darmstadt", "Bastian Bischoff": "Corporate Research, Robert Bosch GmbH, Renningen", "Duy Nguyen-Tuong": "Intel ligent Autonomous Systems Lab, Technische Universit\u00e4t Darmstadt, Darmstadt and Max Planck Institute for Intel ligent Systems, T\u00fcbingen", "Aymeric Dieuleveut": "INRIA, D\u00e9partement d'Informatique de l\u2019ENS, Ecole Normale Sup\u00e9rieure, CNRS, PSL Research University, Paris, France", "Christophe Denis": "LAMA, UMR-CNRS, France", "Sougata Chaudhuri": "Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI", "Thang D. Bui": "Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK", "Josiah Yan": "Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK", "Mengdi Wang": "Department of Computer Science and Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY", "Ji Liu": "Department of Statistics and Department of Industrial and Manufacturing Engineering, Pennsyvania State University, University Park, PA", "Leonard Hasenclever": "Department of Engineering Sciences, University of Oxford, United Kingdom", "Stefan Webb": "Department of Statistics, University of Oxford, United Kingdom", "Thibaut Lienart": "Department of Statistics, University of Oxford, United Kingdom", "Sebastian Vollmer": "DeepMind", "Mohammed Rayyan Sheriff": "Systems and Control Engineering, IIT Bombay, Mumbai, India", "Zuofeng Shang": "Department of Statistics, Purdue University, West Lafayette, IN", "Stephen H. Bach": "DataStax", "Bert Huang": "Computer Science Department, University of California, Santa Cruz, Santa Cruz, CA", "Lin Lin": "Department of Statistics, Pennsylvanian State University, University Park, PA", "Trung Le": "Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Australia", "Tu Dinh Nguyen": "Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Australia", "Vu Nguyen": "Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Australia", "Hariharan Narayanan": "Department of Statistics, University of Pennsylvania", "Stanislas Lauly": "Tencent AI Lab, Shenzhen, Guangdong, China", "Alexandre Allauzen": "D\u00e9partement d\u2019informatique, Universit\u00e9 de Sherbrooke, Sherbrooke, Qu\u00e9bec, Canada", "Shannon Fenn": "School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, Australia", "Shun Zheng": "Department of Computer Science, The University of Chicago, Chicago, Illinois", "Jialei Wang": "Beijing Wisdom Uranium Technology Co., Ltd., Beijing, China", "Wei Xu": "Tencent AI Lab, Shenzhen, China", "Naman Agarwal": "Computer Science Department, Princeton University, Princeton, NJ", "Brian Bullins": "Computer Science Department, Princeton University, Princeton, NJ", "Jiahe Lin": "Department of Statistics and the Informatics, Institute University of Florida, Gainesville, FL", "Zheng-Chu Guo": "Shanghai Key Laboratory for Contemporary Applied Mathematics, School of Mathematical Sciences, Fudan University, Shanghai, P. R. China", "Lei Shi": "School of Mathematical Sciences, Zhejiang University, Hangzhou, China", "Maren Mahsereci": "Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany", "Ricardo Silva": "The Center for Data Science Education and Research, Shiga University, Shiga, Japan and The Institute of Scientific and Industrial Research, Osaka University, Japan", "Mathieu Guillame-Bert": "Auton Lab, The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh", "Qihang Lin": "Department of Computer Science, Princeton University, Princeton, NJ", "Tengyu Ma": "Department of Computer Science, University of Iowa, Iowa City, IA", "Marco Singer": "Institute for Mathematical Stochastics, Georg-August-Universit\u00e4t, G\u00f6ttingen, Germany", "Tatyana Krivobokova": "Institute for Mathematical Stochastics, Georg-August-Universit\u00e4t, G\u00f6ttingen, Germany", "Stanislav Minsker": "Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA", "Sanvesh Srivastava": "Department of Applied and Computational Mathematics and Statistics, The University of Notre Dame, Notre Dame, IN", "Lizhen Lin": "Departments of Statistical Science, Mathematics, and ECE, Duke University, Durham, NC", "Fanny Yang": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Sivaraman Balakrishnan": "Department of Statistics, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA", "Christophe Dupuy": "INRIA, Departement d'Informatique de l'ENS, Ecole normale suprieure, CNRS, PSL Research University, Paris, France", "Valerio Perrone": "Department of Statistics, University of Warwick, Coventry, UK and Department of Computer Science", "Paul A. Jenkins": "Department of Statistics, University of Warwick, Coventry, UK", "Dario Span\u00f2": "Department of Statistics, University of Oxford, United Kingdom", "Eugene Ndiaye": "LTCI, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay, Paris, France", "Olivier Fercoq": "Inria, Universit\u00e9 Paris-Saclay, Palaiseau, France and LTCI, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay, Paris, France", "Alexandre Gramfort": "LTCI, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay, Paris, France", "Th\u00e9o Trouillon": "NAVER LABS Europe, Meylan, France", "\u00c9ric Gaussier": "University College London, London, United Kingdom", "Johannes Welbl": "University College London, London, United Kingdom", "Sebastian Riedel": "Bloomsbury AI, London, United Kingdom and University College London, London, United Kingdom", "Yuting Ma": "Department of Statistics, Columbia University, New York, NY", "K. S. Sesh Kumar": "INRIA, D\u00e9partement d'Informatique de l'Ecole Normale Sup\u00e9rieure (UMR CNRS/ENS/INRIA), Paris, France", "Jean-Baptiste Schiratti": "INSERM, UMRS", "St\u00e9phanie Allassonni\u00e8re": "ARAMIS Lab, INRIA Paris, Sorbonne Universit\u00e9s, UPMC Univ Paris 06 UMRS and Institut du Cerveau et de la Moelle \u00e9pini\u00e8re, ICM, Paris, France", "Olivier Colliot": "ARAMIS Lab, INRIA Paris, Sorbonne Universit\u00e9s, UPMC Univ Paris 06 UMRS and Institut du Cerveau et de la Moelle \u00e9pini\u00e8re, ICM, Paris, France", "Stephan Mandt": "Adobe Research, Adobe Systems Incorporated, San Francisco, CA", "Matthew D. Hoffman": "Department of Statistics, Department of Computer Science, Columbia University, New York, NY", "Will Wei Sun": "Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of No ...", "Christian Wirth": "Computational Learning for Autonomous Systems, Technische Universit\u00e4t Darmstadt, Darmstadt, Germany", "Riad Akrour": "Computational Learning, School of Computer Science, University of Lincoln, Lincoln, Great Britain", "J\u00e9r\u00e9mie Bigot": "Institut de Math\u00e9matiques de Bordeaux, Universit\u00e9 de Bordeaux, Talence, France", "Charles Deledalle": "Institut de Math\u00e9matiques de Bordeaux, Universit\u00e9 de Bordeaux, Talence, France", "Paulo Serra": "Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Amsterdam, the Netherlands", "Michael Riis Andersen": "Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Finland", "Aki Vehtari": "Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark", "Ole Winther": "Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark", "Gregory Darnell": "Google, Palo Alto, CA", "Stoyan Georgiev": "Departments of Statistical Science, Mathematics, and Computer Science, Duke University, Durham, NC", "Sayan Mukherjee": "Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, NJ", "Huishuai Zhang": "Department of ECE, The Ohio State University, Columbus, OH", "Yi Zhou": "Department of ECE, The Ohio State University, Columbus, OH", "Yingbin Liang": "Department of ECE, The Ohio State University, Columbus, OH", "Pietro Coretto": "Department of Statistical Science, University College London, London, United Kingdom", "Yining Wang": "Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA", "Adams Wei Yu": "Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA", "Yaoliang Yu": "Department of Computer Science, University of Illinois at Chicago, Chicago, IL", "Xinhua Zhang": "Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada", "Ruitong Huang": "Independent Researcher, Canberra, Australia", "Andr\u00e1s Gy\u00f6rgy": "Department of Computing Science, University of Alberta, Edmonton, Canada", "Guillaume Lecu\u00e9": "Department of Mathematics, Technion, Haifa, Israel", "Raymond K. W. Wong": "Department of Statistics, University of California, Davis, CA", "James D. Wilson": "Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC", "John Palowitch": "Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC", "Shankar Bhamidi": "Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC", "Felix X. Yu": "University of Utah, Salt Lake City, UT", "Aditya Bhaskara": "Google Research, New York, NY", "Sanjiv Kumar": "Snap, Inc., Venice, CA", "Andrew C. Heusser": "Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH", "Kirsten Ziman": "Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH", "Lucy L. W. Owen": "Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH", "At\u0131l\u0131m G\u00fcnes Baydin": "Department of Computer Science, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland", "Barak A. Pearlmutter": "Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA", "Alexey Andreyevich Radul": "School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN", "Wesley Cowan": "Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-shi, Chiba, Japan", "Junya Honda": "Department of Management Science and Information Systems, Rutgers University, Piscataway, NJ", "Nagarajan Natarajan": "Dept. of Computer Science, University of Texas at Austin, Austin, TX", "Inderjit S. Dhillon": "Machine Learning Dept., Carnegie Mellon University, Pittsburgh, PA", "Pradeep Ravikumar": "Dept. of Statistics, and Dept. of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI", "Elif Vural": "Centre de Recherche INRIA Bretagne Atlantique, Campus Universitaire de Beaulieu, Rennes, France", "Valeria Vitelli": "Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway", "\u00d8ystein S\u00f8rensen": "Department of Decision Sciences, Bocconi University, Milan, Italy", "Marta Crispino": "Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway", "Arnoldo Frigessi": "Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway", "Fr\u00e9d\u00e9ric Chazal": "Computer Science Department, Montana State University, Bozeman, MT", "Brittany Fasy": "Ecole Centrale de Nantes, Laboratoire de math\u00e9matiques Jean Leray, Nantes France", "Bertrand Michel": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Alessandro Rinaldo": "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA", "Mario Lucic": "Department of Electrical Engineering and Computer Sciences, Caltech, Pasadena, California", "Matthew Faulkner": "Department of Computer Science, ETH Zurich, Z\u00fcrich, Switzerland", "Andreas Krause": "Department of Computer Science, University of Haifa, Haifa, Israel", "Vivek S. Borkar": "Department of Electrical Engineering, Stanford", "Vikranth R. Dwaracherla": "Department of Mathematical Sciences, Indian Institute of Science Education and Research, Mohali, India", "Clint P. George": "Department of Statistics, University of Florida, Gainesville, FL", "Alan Morningstar": "Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada", "Qianxiao Li": "Peking University, Beijing, China", "Long Chen": "Beijing Institute of Big Data Research and Peking University, Beijing, China", "Cheng Tai": "Princeton University, Princeton, NJ and Beijing Institute of Big Data Research and Peking University, Beijing, China", "Xiao-Tong Yuan": "Baidu Research, Bellevue, WA", "Lucas Janson": "Aeronautics and Astronautics, Stanford University, Stanford, CA", "Siqi Wu": "Department of Statistics, Electrical Engineering & Computer Science, University of California, Berkeley, CA", "Fred Morstatter": "Arizona State University, Tempe, AZ", "Yunwen Lei": "School of Mathematical Sciences, Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai, China", "Jian Du": "Department of Electrical and Computer Engineering, University of Macau, Avenida da Universidade, Taipa, Macau", "Shaodan Ma": "Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong", "Yik-Chung Wu": "Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA", "Soummya Kar": "Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA", "Michael Freitag": "Chair of Embedded Intelligence for Health Care & Wellbeing, Augsburg University, Augsburg, Germany & Chair of Complex & Intelligent Systems, Universit\u00e4t Passau, Passau, Germany an ...", "Shahin Amiriparian": "Chair of Embedded Intelligence for Health Care & Wellbeing, Augsburg University, Augsburg, Germany & Chair of Complex & Intelligent Systems, Universit\u00e4t Passau, Passau, Germany an ...", "Sergey Pugachevskiy": "Chair of Embedded Intelligence for Health Care & Wellbeing, Augsburg University, Augsburg, Germany & Chair of Complex & Intelligent Systems, Universit\u00e4t Passau, Passau, Germany an ...", "Nicholas Cummins": "Chair of Embedded Intelligence for Health Care & Wellbeing, Augsburg University, Augsburg, Germany & Chair of Complex & Intelligent Systems, Universit\u00e4t Passau, Passau, Germany an ...", "Sarah Nogueira": "School of Computer Science, University of Manchester, Manchester, UK", "Konstantinos Sechidis": "School of Computer Science, University of Manchester, Manchester, UK", "Christopher Tosh": "Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA", "Oscar Hernan Madrid Padilla": "Department of Statistics, University of California, Davis, CA", "James Sharpnack": "Department of Information, Risk, and Operations Management, Department of Statistics and Data Sciences, University of Texas, Austin, TX", "Rajen D. Shah": "Seminar f\u00fcr Statistik, ETH Z\u00fcrich Z\u00fcrich, Switzerland", "Quanming Yao": "Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong", "Hiroaki Sasaki": "Department of Mathematical and Computing Science, Tokyo Institute of Technology, Tokyo, Japan and Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan", "Takafumi Kanamori": "University College London, London, United Kingdom and Department of Computer Science, University of Helsinki, Helsinki, Finland and Canadian Institute for Advanced Research", "Gang Niu": "Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan and Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan", "Heng Lian": "Department of Statistics and Applied Probability, National University of Singapore, Singapore", "Zifan Li": "Department of Statistics, University of Michigan, Ann Arbor, MI", "Xingguo Li": "School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA", "Tuo Zhao": "Department of Computer Science, Johns Hopkins University, Baltimore, MD", "Raman Arora": "Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL", "Han Liu": "Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN", "Lisha Li": "University of Washington, Seattle, WA", "Kevin Jamieson": "Google Research, New York, NY", "Giulia DeSalvo": "Google Research, New York, NY", "Afshin Rostamizadeh": "Carnegie Mellon University, Pittsburgh, PA", "Bruce Hajek": "Department of Statistics and Data Science, Yale University, New Haven, CT", "Yihong Wu": "Krannert School of Management, Purdue University, West Lafayette, IN", "Itay Hubara": "Department of Computer Science and Department of Statistics, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, Canada", "Matthieu Courbariaux": "Department of Statistics, Columbia University, New York", "Daniel Soudry": "Department of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel", "Ran El-Yaniv": "Department of Computer Science and Department of Statistics, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, Canada", "Genki Kusano": "The Institute of Statistical Mathematics, Tokyo, Japan", "Benjamin Guedj": "Modal project-team, Lille - Nord Europe research center, Inria, France", "Simone Filice": "DIE, University of Roma, Tor Vergata, Italy", "Giuseppe Castellucci": "Qatar Computing Research Institute, HKBU, Qatar", "Giovanni Da San Martino": "Amazon", "Danilo Croce": "DII, University of Roma, Tor Vergata, Italy", "Massil Achab": "Centre de Recherche en Math\u00e9matique de la D\u00e9cision, Universit\u00e9 Paris-Dauphine, Paris, France and Centre de Math\u00e9matiques Appliqu\u00e9es, Ecole polytechnique, Palaiseau, France", "Emmanuel Bacry": "Centre de Math\u00e9matiques Appliqu\u00e9es, \u00c9cole polytechnique, Palaiseau, France", "St\u00e9phane Ga\u00efffas": "Centre de Math\u00e9matiques Appliqu\u00e9es, \u00c9cole polytechnique, Palaiseau, France", "Sivan Sabato": "Lassonde School of Engineeging, EECS Department, York University, Toronto, ON, Canada", "Colin Pawlowski": "Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA", "Nicholas Boyd": "Department of Statistics, Stanford University, Stanford, CA", "Trevor Hastie": "Department of Electrical Engineering, Stanford University, Stanford, CA", "Benjamin Recht": "Division of Computer Science and Department of Statistics, University of California, Berkeley, CA", "Andrei Patrascu": "Automatic Control and Systems Engineering Department, University Politehnica of Bucharest, Bucharest", "Nihar B. Shah": "Department of Electrical Engineering and Computer Sciences and Department of Statistics, University of California, Berkeley, CA", "David P. Helmbold": "Google, Mountain View, CA", "Boris Konev": "Department of Computer Science, University of Bremen, Germany", "Carsten Lutz": "Department of Computer Science, Dresden University of Technology, Germany", "Ana Ozaki": "Department of Computer Science, University of Liverpool, United Kingdom", "Shuhan Liang": "Department of Statistics, North Carolina State University, Raleigh, NC", "Wenbin Lu": "Department of Statistics, North Carolina State University, Raleigh, NC", "Rui Song": "School of Statistics, University of Minnesota, Minneapolis, MN", "Junwei Lu": "Booth School of Business, The University of Chicago, Chicago, IL", "Mladen Kolar": "Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ", "Quan Zhang": "Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX", "Ali Zarezade": "Max Planck Institute for Software Systems, Kaiserslautern, Germany", "Abir De": "Max Planck Institute for Software Systems, Kaiserslautern, Germany", "Utkarsh Upadhyay": "Sharif University of Technology, Teheran, Iran", "Hamid R. Rabiee": "Max Planck Institute for Software Systems, Kaiserslautern, Germany", "Avik Ray": "Department of Mathematics, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, Bonn, Germany", "Joe Neeman": "Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX", "Sujay Sanghavi": "Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX", "Jianqing Fan": "Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ", "Weichen Wang": "Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ", "Jie Shen": "Baidu Research, Bellevue, WA", "Davoud Ataee Tarzanagh": "Department of Statistics, UF Informatics Institute, University of Florida, Gainesville, FL", "Christian Borgs": "Microsoft Research, Cambridge, MA", "Jennifer T. Chayes": "Microsoft Research, Cambridge, MA", "Henry Cohn": "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA", "Jiyan Yang": "Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA", "Yin-Lam Chow": "Department of Computer Science, Stanford University, Stanford, CA", "Christopher R\u00e9": "International Computer Science Institute and Department of Statistics, University of California, Berkeley, Berkeley, CA", "Hongzhou Lin": "Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France", "Julien Mairal": "University of Washington, Department of Statistics, Seattle, WA", "Amichai Painsky": "School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem, Israel", "Martin Bompaire": "Centre de Math\u00e9matiques Appliqu\u00e9es, \u00c9cole polytechnique, Palaiseau, France", "Philip Deegan": "Centre de Math\u00e9matiques Appliqu\u00e9es, \u00c9cole polytechnique, Palaiseau, France", "Swapna Buccapatnam": "Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH", "Fang Liu": "Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH", "Atilla Eryilmaz": "Department of Electrical and Computer Engineering and Computer Science Engineering, The Ohio State University, Columbus, OH", "Botao Hao": "Department of Management Science, University of Miami School of Business Administration, Miami, FL", "Yufeng Liu": "Department of Statistics, Purdue University, West Lafayette, IN", "Shusen Wang": "Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY", "Alex Gittens": "International Computer Science Institute and Department of Statistics, University of California at Berkeley, Berkeley, CA", "Emilija Perkovic": "Institute for Computing and Information Sciences and Department of Tumor Immunology, Radboud, University Medical Center, Nijmegen, The Netherlands", "Johannes Textor": "Seminar for Statistics, ETH Zurich, Switzerland", "Markus Kalisch": "Seminar for Statistics, ETH Zurich, Switzerland", "Alon Gonen": "School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel", "Prateek Jain": "Microsoft Research, Bangalore, India", "Praneeth Netrapalli": "Paul G. Allen School of Computer Science and Department of Statistics, University of Washington, Seattle, WA", "Sham M. Kakade": "Department of Electrical Engineering, University of Washington, Seattle, WA", "Rahul Kidambi": "Department of Management Science and Engineering, Stanford University, Palo Alto, CA", "Gunwoong Park": "Department of Statistics, Department of Computer Science, Wisconsin Institute for Discovery, Optimization Group, University of Wisconsin, Madison, WI", "Hafiz Tiomoko Ali": "CentraleSup\u00e9lec, Universit\u00e9 Paris Saclay, Laboratoire des Signaux et Syst\u00e9mes, Gif-Sur-Yvette", "Avanti Athreya": "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", "Minh Tang": "Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD", "Carey E. Priebe": "Center for Imaging Science, Johns Hopkins University, Baltimore, MD", "Youngser Park": "Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD", "Joshua T. Vogelstein": "Department of Statistics, University of Michigan, Ann Arbor, MI", "Keith Levin": "Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA", "Vince Lyzinski": "Department of Operations, Business Analytics, and Information Systems, College of Business, University of Cincinnati, Cincinnati, OH", "Maik D\u00f6ring": "Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Budapest, Hungary", "L\u00e1szl\u00f3 Gy\u00f6rfi": "Institute of Stochastic and Applications, University of Stuttgart, Stuttgart, Germany"}, {"Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan": "Japan", "NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan": "Japan", "Department of Computer Science, Princeton University, Princeton, NJ": "America", "School of Computer Science, Carnegie Mellon University, Pittsburgh, PA": "America", "MPI for Software Systems, Kaiserslautern, Germany": "Germany", "College of Computing, Georgia Institute of Technology, Atlanta, GA": "America", "School of Computer Science, Carnegie Mellon University": "Carnegie Mellon University", "Google, NY": "America", "Department of Computer Science, Stanford University, Stanford, CA": "America", "Department of Electrical Engineering, Stanford University, Stanford, CA": "America", "Department of Computer Science, Dartmouth University, Hanover, NH": "America", "Department of Statistics, University of Michigan, Ann Arbor, MI": "America", "Department of Statistics, Stanford University, Stanford, CA": "America", "Dipartimento di Fisica, INFN-Sezione di Roma1 and CNR-Nanotec, La Sapienza University of Rome, Rome, Italy": "Italy", "Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA": "America", "School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA": "America", "Department of Mathematics, Colorado State University, Fort Collins, CO": "America", "Department of Mathematics and Computer Science, Wilkes University, Wilkes-Barre, PA": "America", "Department of Mathematics, Texas Christian University, Fort Worth, TX": "America", "Department of Mathematics, Duke University, Durham, NC": "America", "Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, MN": "America", "Department of Mathematics, University of Minnesota, Twin Cities, Minneapolis, MN": "America", "Department of Mathematics, University of Central Florida, Orlando, FL": "America", "LTCI, CNRS, Telecom ParisTech, Universit\u00e9 Paris-Saclay, Paris, France": "France", "CMAP, INRIA XPOP, Ecole Polytechnique, Palaiseau, France": "France", "Google, Inc., Mountain View, CA": "America", "School of Mathematics & Statistics, The University of Melbourne, Parkville, VIC, Australia": "Australia", "Department of Computer Science & Engineering, Chalmers University of Technology, Gothenburg, Sweden": "Sweden", "School of Computing & Information Systems, The University of Melbourne, Parkville, VIC, Australia": "Australia", "Department of Computer Science, Brown University, Providence, RI": "America", "Department of Computer Science, Columbia University, New York, NY": "America", "Data Science Institute, Departments of Political Science and Statistics, Columbia University, New York, NY": "America", "Data Science Institute, Departments of Computer Science and Statistics, Columbia University, New York, NY": "America", "School of Computing Sciences, University of East Anglia, Norwich, UK": "America", "Department of Mathematical Analysis and Statistical Inference, The Institute of Statistical Mathematics, Tokyo, Japan": "Japan", "Department of Mathematics, National University of Singapore, Singapore": "Singapore", "ShoppeAI, Toronto, Ontario, Canada": "Canada", "Univ. 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China", "Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong": "Hong Kong", "Department of Mathematics, Ruhr-University Bochum": "Ruhr-University Bochum", "Department of Computer Science, University of Regina": "University of Regina", "Department of Statistics, Carnegie Mellon University, Pittsburgh, PA": "America", "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 ...": "Chin ...", "Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA": "America", "Georgetown University, Washington D.C.": "Washington D.C.", "Department of Electrical and Computer Engineering, Northeastern University, Boston, MA": "America", "Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN": "America", "Department of Mathematics and Statistics, University 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of Electrical Engineering, University of Texas at Dallas, Richardson, TX": "America", "Department of Systems Engineering, University of Texas at Dallas, Richardson, TX and Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India": "India", "D\u00e9partement d'informatique de l'ENS, \u00c9cole normale sup\u00e9rieure, CNRS, PSL Research University, Paris, France": "France", "LTCI, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay INRIA, Universit\u00e9 Paris-Saclay, Saclay, France": "France", "Department of Computing and Software Systems, Nagoya University, Nagoya, Japan": "Japan", "The Institute of Statistical Mathematics, Tokyo, Japan": "Japan", "University College London, London, UK": "America", "Department of Computer Science, University of T\u00fcbingen, T\u00fcbingen, Germany": "Germany", "Cerebras Systems": "erebras Systems", "University of Pennsylvania": "niversity of Pennsylvania", "Yale University": "ale University", 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R. China": "P. R. 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