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JMLR Volume 18

Averaged Collapsed Variational Bayes Inference
Katsuhiko Ishiguro, Issei Sato, Naonori Ueda; (1):1−29, 2017.
[abs][pdf][bib]

Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
Nan Du, Yingyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song; (2):1−45, 2017.
[abs][pdf][bib]

Local algorithms for interactive clustering
Pranjal Awasthi, Maria Florina Balcan, Konstantin Voevodski; (3):1−35, 2017.
[abs][pdf][bib]

SnapVX: A Network-Based Convex Optimization Solver
David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec; (4):1−5, 2017.
[abs][pdf][bib]      [code] [stanford.edu]

Communication-efficient Sparse Regression
Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor; (5):1−30, 2017.
[abs][pdf][bib]

Improving Variational Methods via Pairwise Linear Response Identities
Jack Raymond, Federico Ricci-Tersenghi; (6):1−36, 2017.
[abs][pdf][bib]

Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Adam S. Charles, Dong Yin, Christopher J. Rozell; (7):1−37, 2017.
[abs][pdf][bib]

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; (8):1−35, 2017.
[abs][pdf][bib]

Spectral Clustering Based on Local PCA
Ery Arias-Castro, Gilad Lerman, Teng Zhang; (9):1−57, 2017.
[abs][pdf][bib]

On Perturbed Proximal Gradient Algorithms
Yves F. Atchadé, Gersende Fort, Eric Moulines; (10):1−33, 2017.
[abs][pdf][bib]

Differential Privacy for Bayesian Inference through Posterior Sampling
Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein; (11):1−39, 2017.
[abs][pdf][bib]

Refinery: An Open Source Topic Modeling Web Platform
Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; (12):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns
Herbert Jaeger; (13):1−43, 2017.
[abs][pdf][bib]      [supplementary]

Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei; (14):1−45, 2017.
[abs][pdf][bib]

Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters
Jacques Wainer, Gavin Cawley; (15):1−35, 2017.
[abs][pdf][bib]

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification
Naoki Ito, Akiko Takeda, Kim-Chuan Toh; (16):1−49, 2017.
[abs][pdf][bib]

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas; (17):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen; (18):1−65, 2017.
[abs][pdf][bib]

Breaking the Curse of Dimensionality with Convex Neural Networks
Francis Bach; (19):1−53, 2017.
[abs][pdf][bib]

Memory Efficient Kernel Approximation
Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon; (20):1−32, 2017.
[abs][pdf][bib]

On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
Francis Bach; (21):1−38, 2017.
[abs][pdf][bib]

Analyzing Tensor Power Method Dynamics in Overcomplete Regime
Animashree An, kumar, Rong Ge, Majid Janzamin; (22):1−40, 2017.
[abs][pdf][bib]

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
Edward Raff; (23):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

Identifying a Minimal Class of Models for High--dimensional Data
Daniel Nevo, Ya'acov Ritov; (24):1−29, 2017.
[abs][pdf][bib]

Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; (25):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

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; (26):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

Generalized Pólya Urn for Time-Varying Pitman-Yor Processes
François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy; (27):1−32, 2017.
[abs][pdf][bib]

Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Alex, re Bouchard-Côté, Arnaud Doucet, Andrew Roth; (28):1−39, 2017.
[abs][pdf][bib]

Certifiably Optimal Low Rank Factor Analysis
Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder; (29):1−53, 2017.
[abs][pdf][bib]

Group Sparse Optimization via lp,q Regularization
Yaohua Hu, Chong Li, Kaiwen Meng, Jing Qin, Xiaoqi Yang; (30):1−52, 2017.
[abs][pdf][bib]

Preference-based Teaching
Ziyuan Gao, Christoph Ries, Hans U. Simon, S, ra Zilles; (31):1−32, 2017.
[abs][pdf][bib]

Nonparametric Risk Bounds for Time-Series Forecasting
Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish; (32):1−40, 2017.
[abs][pdf][bib]

Online Bayesian Passive-Aggressive Learning
Tianlin Shi, Jun Zhu; (33):1−39, 2017.
[abs][pdf][bib]

Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning
Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy; (34):1−41, 2017.
[abs][pdf][bib]

A Spectral Algorithm for Inference in Hidden semi-Markov Models
Igor Melnyk, Arindam Banerjee; (35):1−39, 2017.
[abs][pdf][bib]

Simplifying Probabilistic Expressions in Causal Inference
Santtu Tikka, Juha Karvanen; (36):1−30, 2017.
[abs][pdf][bib]

Nearly optimal classification for semimetrics
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch; (37):1−22, 2017.
[abs][pdf][bib]

Bridging Supervised Learning and Test-Based Co-optimization
Elena Popovici; (38):1−39, 2017.
[abs][pdf][bib]      [appendix]

GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Eemeli Lepp{ä}aho, Muhammad Ammad-ud-din, Samuel Kaski; (39):1−5, 2017.
[abs][pdf][bib]      [code] [r-project.org]

GPflow: A Gaussian Process Library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman; (40):1−6, 2017.
[abs][pdf][bib]      [code] [webpage]

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; (41):1−49, 2017.
[abs][pdf][bib]

Learning Local Dependence In Ordered Data
Guo Yu, Jacob Bien; (42):1−60, 2017.
[abs][pdf][bib]

Bayesian Learning of Dynamic Multilayer Networks
Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts; (43):1−29, 2017.
[abs][pdf][bib]

Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality
Samory Kpotufe, Nakul Verma; (44):1−29, 2017.
[abs][pdf][bib]

Asymptotic behavior of Support Vector Machine for spiked population model
Hanwen Huang; (45):1−21, 2017.
[abs][pdf][bib]

Distributed Semi-supervised Learning with Kernel Ridge Regression
Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou; (46):1−22, 2017.
[abs][pdf][bib]

On Markov chain Monte Carlo methods for tall data
R{é}mi Bardenet, Arnaud Doucet, Chris Holmes; (47):1−43, 2017.
[abs][pdf][bib]

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease; (48):1−33, 2017.
[abs][pdf][bib]

Clustering from General Pairwise Observations with Applications to Time-varying Graphs
Shiau Hong Lim, Yudong Chen, Huan Xu; (49):1−47, 2017.
[abs][pdf][bib]

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
Debarghya Ghoshdastidar, Ambedkar Dukkipati; (50):1−41, 2017.
[abs][pdf][bib]

Reconstructing Undirected Graphs from Eigenspaces
Yohann De Castro, Thibault Espinasse, Paul Rochet; (51):1−24, 2017.
[abs][pdf][bib]

An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
Ohad Shamir; (52):1−11, 2017.
[abs][pdf][bib]

Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
Adel Javanmard; (53):1−31, 2017.
[abs][pdf][bib]

Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar; (54):1−24, 2017.
[abs][pdf][bib]

On the Consistency of Ordinal Regression Methods
Fabian Pedregosa, Francis Bach, Alex, re Gramfort; (55):1−35, 2017.
[abs][pdf][bib]

Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences
Takashi Takenouchi, Takafumi Kanamori; (56):1−26, 2017.
[abs][pdf][bib]

Density Estimation in Infinite Dimensional Exponential Families
Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar; (57):1−59, 2017.
[abs][pdf][bib]

Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis
Matth{ä}us Kleindessner, Ulrike von Luxburg; (58):1−52, 2017.
[abs][pdf][bib]

Joint Label Inference in Networks
Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy; (59):1−39, 2017.
[abs][pdf][bib]

Achieving Optimal Misclassification Proportion in Stochastic Block Models
Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou; (60):1−45, 2017.
[abs][pdf][bib]

On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks
Prakash Balach, ran, Eric D. Kolaczyk, Weston D. Viles; (61):1−33, 2017.
[abs][pdf][bib]

Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas; (62):1−58, 2017.
[abs][pdf][bib]

Fundamental Conditions for Low-CP-Rank Tensor Completion
Morteza Ashraphijuo, Xiaodong Wang; (63):1−29, 2017.
[abs][pdf][bib]

Parallel Symmetric Class Expression Learning
An C. Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsl, ; (64):1−34, 2017.
[abs][pdf][bib]

Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning
Jervis Pinto, Alan Fern; (65):1−35, 2017.
[abs][pdf][bib]

Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron, Vikas Sindhwani; (66):1−42, 2017.
[abs][pdf][bib]

Sharp Oracle Inequalities for Square Root Regularization
Benjamin Stucky, Sara van de Geer; (67):1−29, 2017.
[abs][pdf][bib]

Soft Margin Support Vector Classification as Buffered Probability Minimization
Matthew Norton, Alex, er Mafusalov, Stan Uryasev; (68):1−43, 2017.
[abs][pdf][bib]

Variational Particle Approximations
Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman; (69):1−29, 2017.
[abs][pdf][bib]

A Bayesian Framework for Learning Rule Sets for Interpretable Classification
Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille; (70):1−37, 2017.
[abs][pdf][bib]

A Robust-Equitable Measure for Feature Ranking and Selection
A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang; (71):1−46, 2017.
[abs][pdf][bib]

Multiscale Strategies for Computing Optimal Transport
Samuel Gerber, Mauro Maggioni; (72):1−32, 2017.
[abs][pdf][bib]

Non-parametric Policy Search with Limited Information Loss
Herke van Hoof, Gerhard Neumann, Jan Peters; (73):1−46, 2017.
[abs][pdf][bib]

Tests of Mutual or Serial Independence of Random Vectors with Applications
Martin Bilodeau, Aur{é}lien Guetsop Nangue; (74):1−40, 2017.
[abs][pdf][bib]      [supplementary]

Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail; (75):1−34, 2017.
[abs][pdf][bib]

Quantifying the Informativeness of Similarity Measurements
Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas; (76):1−61, 2017.
[abs][pdf][bib]

Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis
Alessio Benavoli, Giorgio Corani, Janez Dem\v{s}ar, Marco Zaffalon; (77):1−36, 2017.
[abs][pdf][bib]

Relational Reinforcement Learning for Planning with Exogenous Effects
David Martínez, Guillem Alenyà, Tony Ribeiro, Katsumi Inoue, Carme Torras; (78):1−44, 2017.
[abs][pdf][bib]

Bayesian Tensor Regression
Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson; (79):1−31, 2017.
[abs][pdf][bib]

Robust Discriminative Clustering with Sparse Regularizers
Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach; (80):1−50, 2017.
[abs][pdf][bib]

Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions
Weiwei Liu, Ivor W. Tsang; (81):1−36, 2017.
[abs][pdf][bib]

Learning Scalable Deep Kernels with Recurrent Structure
Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing; (82):1−37, 2017.
[abs][pdf][bib]

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Vardan Papyan, Yaniv Romano, Michael Elad; (83):1−52, 2017.
[abs][pdf][bib]

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Lin Xiao; (84):1−42, 2017.
[abs][pdf][bib]

Angle-based Multicategory Distance-weighted SVM
Hui Sun, Bruce A. Craig, Lingsong Zhang; (85):1−21, 2017.
[abs][pdf][bib]

Minimax Estimation of Kernel Mean Embeddings
Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Mu, et; (86):1−47, 2017.
[abs][pdf][bib]

The Impact of Random Models on Clustering Similarity
Alexander J. Gates, Yong-Yeol Ahn; (87):1−28, 2017.
[abs][pdf][bib]

Hierarchical Clustering via Spreading Metrics
Aurko Roy, Sebastian Pokutta; (88):1−35, 2017.
[abs][pdf][bib]

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\~{a}o V. Messias; (89):1−5, 2017.
[abs][pdf][bib]      [code]

A survey of Algorithms and Analysis for Adaptive Online Learning
H. Brendan McMahan; (90):1−50, 2017.
[abs][pdf][bib]

A distributed block coordinate descent method for training l1 regularized linear classifiers
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan; (91):1−35, 2017.
[abs][pdf][bib]

Distributed Learning with Regularized Least Squares
Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou; (92):1−31, 2017.
[abs][pdf][bib]

Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman; (93):1−67, 2017.
[abs][pdf][bib]

An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller; (94):1−38, 2017.
[abs][pdf][bib]

Fisher Consistency for Prior Probability Shift
Dirk Tasche; (95):1−32, 2017.
[abs][pdf][bib]

openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
Maximilian Schmitt, Bj{ö}rn Schuller; (96):1−5, 2017.
[abs][pdf][bib]      [code]

Optimal Rates for Multi-pass Stochastic Gradient Methods
Junhong Lin, Lorenzo Rosasco; (97):1−47, 2017.
[abs][pdf][bib]

Rank Determination for Low-Rank Data Completion
Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal; (98):1−29, 2017.
[abs][pdf][bib]

Bayesian Network Learning via Topological Order
Young Woong Park, Diego Klabjan; (99):1−32, 2017.
[abs][pdf][bib]

Stability of Controllers for Gaussian Process Dynamics
Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters; (100):1−37, 2017.
[abs][pdf][bib]

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach; (101):1−51, 2017.
[abs][pdf][bib]

Confidence Sets with Expected Sizes for Multiclass Classification
Christophe Denis, Mohamed Hebiri; (102):1−28, 2017.
[abs][pdf][bib]

Online Learning to Rank with Top-k Feedback
Sougata Chaudhuri, Ambuj Tewari; (103):1−50, 2017.
[abs][pdf][bib]

A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Thang D. Bui, Josiah Yan, Richard E. Turner; (104):1−72, 2017.
[abs][pdf][bib]

Accelerating Stochastic Composition Optimization
Mengdi Wang, Ji Liu, Ethan X. Fang; (105):1−23, 2017.
[abs][pdf][bib]

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; (106):1−37, 2017.
[abs][pdf][bib]

Optimal Dictionary for Least Squares Representation
Mohammed Rayyan Sheriff, Debasish Chatterjee; (107):1−28, 2017.
[abs][pdf][bib]

Computational Limits of A Distributed Algorithm for Smoothing Spline
Zuofeng Shang, Guang Cheng; (108):1−37, 2017.
[abs][pdf][bib]

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor; (109):1−67, 2017.
[abs][pdf][bib]

Clustering with Hidden Markov Model on Variable Blocks
Lin Lin, Jia Li; (110):1−49, 2017.
[abs][pdf][bib]

Approximation Vector Machines for Large-scale Online Learning
Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung; (111):1−55, 2017.
[abs][pdf][bib]

Efficient Sampling from Time-Varying Log-Concave Distributions
Hariharan Narayanan, Alex, er Rakhlin; (112):1−29, 2017.
[abs][pdf][bib]

Document Neural Autoregressive Distribution Estimation
Stanislas Lauly, Yin Zheng, Alex, re Allauzen, Hugo Larochelle; (113):1−24, 2017.
[abs][pdf][bib]

Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
Shannon Fenn, Pablo Moscato; (114):1−26, 2017.
[abs][pdf][bib]

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang; (115):1−52, 2017.
[abs][pdf][bib]

Second-Order Stochastic Optimization for Machine Learning in Linear Time
Naman Agarwal, Brian Bullins, Elad Hazan; (116):1−40, 2017.
[abs][pdf][bib]

Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
Jiahe Lin, George Michailidis; (117):1−49, 2017.
[abs][pdf][bib]

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Zheng-Chu Guo, Lei Shi, Qiang Wu; (118):1−25, 2017.
[abs][pdf][bib]

Probabilistic Line Searches for Stochastic Optimization
Maren Mahsereci, Philipp Hennig; (119):1−59, 2017.
[abs][pdf][bib]

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
Ricardo Silva, Shohei Shimizu; (120):1−49, 2017.
[abs][pdf][bib]

Classification of Time Sequences using Graphs of Temporal Constraints
Mathieu Guillame-Bert, Artur Dubrawski; (121):1−34, 2017.
[abs][pdf][bib]

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang; (122):1−43, 2017.
[abs][pdf][bib]

Kernel Partial Least Squares for Stationary Data
Marco Singer, Tatyana Krivobokova, Axel Munk; (123):1−41, 2017.
[abs][pdf][bib]

Robust and Scalable Bayes via a Median of Subset Posterior Measures
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson; (124):1−40, 2017.
[abs][pdf][bib]

Statistical and Computational Guarantees for the Baum-Welch Algorithm
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright; (125):1−53, 2017.
[abs][pdf][bib]

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
Christophe Dupuy, Francis Bach; (126):1−45, 2017.
[abs][pdf][bib]

Poisson Random Fields for Dynamic Feature Models
Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh; (127):1−45, 2017.
[abs][pdf][bib]

Gap Safe Screening Rules for Sparsity Enforcing Penalties
Eugene Ndiaye, Olivier Fercoq, Alex, re Gramfort, Joseph Salmon; (128):1−33, 2017.
[abs][pdf][bib]

Minimax Filter: Learning to Preserve Privacy from Inference Attacks
Jihun Hamm; (129):1−31, 2017.
[abs][pdf][bib]

Knowledge Graph Completion via Complex Tensor Factorization
Th{é}o Trouillon, Christopher R. Dance, {\'E}ric Gaussier, Johannes Welbl, Sebastian Riedel, Guillaume Bouchard; (130):1−38, 2017.
[abs][pdf][bib]

Stabilized Sparse Online Learning for Sparse Data
Yuting Ma, Tian Zheng; (131):1−36, 2017.
[abs][pdf][bib]

Active-set Methods for Submodular Minimization Problems
K. S. Sesh Kumar, Francis Bach; (132):1−31, 2017.
[abs][pdf][bib]

A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations
Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, Stanley Durrleman; (133):1−33, 2017.
[abs][pdf][bib]

Stochastic Gradient Descent as Approximate Bayesian Inference
Stephan M, t, Matthew D. Hoffman, David M. Blei; (134):1−35, 2017.
[abs][pdf][bib]

STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Will Wei Sun, Lexin Li; (135):1−37, 2017.
[abs][pdf][bib]

A Survey of Preference-Based Reinforcement Learning Methods
Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes F{{ü}}rnkranz; (136):1−46, 2017.
[abs][pdf][bib]

Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
Jérémie Bigot, Charles Deledalle, Delphine F{é}ral; (137):1−50, 2017.
[abs][pdf][bib]

Dimension Estimation Using Random Connection Models
Paulo Serra, Michel M, jes; (138):1−35, 2017.
[abs][pdf][bib]

Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen; (139):1−58, 2017.
[abs][pdf][bib]

Adaptive Randomized Dimension Reduction on Massive Data
Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt; (140):1−30, 2017.
[abs][pdf][bib]

A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms
Huishuai Zhang, Yingbin Liang, Yuejie Chi; (141):1−35, 2017.
[abs][pdf][bib]

Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering
Pietro Coretto, Christian Hennig; (142):1−39, 2017.
[abs][pdf][bib]

On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
Yining Wang, Adams Wei Yu, Aarti Singh; (143):1−41, 2017.
[abs][pdf][bib]

Generalized Conditional Gradient for Sparse Estimation
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans; (144):1−46, 2017.
[abs][pdf][bib]

Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang, Tor Lattimore, Andr{á}s Gy{ö}rgy, Csaba Szepesv{á}ri; (145):1−31, 2017.
[abs][pdf][bib]

Regularization and the small-ball method II: complexity dependent error rates
Guillaume Lecu{é}, Shahar Mendelson; (146):1−48, 2017.
[abs][pdf][bib]

Matrix Completion with Noisy Entries and Outliers
Raymond K. W. Wong, Thomas C. M. Lee; (147):1−25, 2017.
[abs][pdf][bib]

Faithfulness of Probability Distributions and Graphs
Kayvan Sadeghi; (148):1−29, 2017.
[abs][pdf][bib]

Community Extraction in Multilayer Networks with Heterogeneous Community Structure
James D. Wilson, John Palowitch, Shankar Bhamidi, Andrew B. Nobel; (149):1−49, 2017.
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On Binary Embedding using Circulant Matrices
Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang; (150):1−30, 2018.
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Variational Fourier Features for Gaussian Processes
James Hensman, Nicolas Durr, e, Arno Solin; (151):1−52, 2018.
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HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning; (152):1−6, 2018.
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Automatic Differentiation in Machine Learning: a Survey
Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind; (153):1−43, 2018.
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Normal Bandits of Unknown Means and Variances
Wesley Cowan, Junya Honda, Michael N. Katehakis; (154):1−28, 2018.
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Cost-Sensitive Learning with Noisy Labels
Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari; (155):1−33, 2018.
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Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
Yining Wang, Aarti Singh; (156):1−42, 2018.
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A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning
Elif Vural, Christine Guillemot; (157):1−55, 2018.
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Probabilistic preference learning with the Mallows rank model
Valeria Vitelli, {Ø}ystein S{ø}rensen, Marta Crispino, Arnoldo Frigessi, Elja Arjas; (158):1−49, 2018.
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Robust Topological Inference: Distance To a Measure and Kernel Distance
Frédéric Chazal, Brittany Fasy, Fabrizio Lecci, Bertr, Michel, Aless, ro Rinaldo, Larry Wasserman; (159):1−40, 2018.
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Training Gaussian Mixture Models at Scale via Coresets
Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman; (160):1−25, 2018.
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Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe; (161):1−27, 2018.
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Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model
Clint P. George, Hani Doss; (162):1−38, 2018.
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Deep Learning the Ising Model Near Criticality
Alan Morningstar, Roger G. Melko; (163):1−17, 2018.
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pomegranate: Fast and Flexible Probabilistic Modeling in Python
Jacob Schreiber; (164):1−6, 2018.
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Maximum Principle Based Algorithms for Deep Learning
Qianxiao Li, Long Chen, Cheng Tai, Weinan E; (165):1−29, 2018.
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Gradient Hard Thresholding Pursuit
Xiao-Tong Yuan, Ping Li, Tong Zhang; (166):1−43, 2018.
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Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone; (167):1−51, 2018.
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Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition
Siqi Wu, Bin Yu; (168):1−56, 2018.
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In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics
Fred Morstatter, Huan Liu; (169):1−32, 2018.
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On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness
Fabrizio Angiulli; (170):1−60, 2018.
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Convergence of Unregularized Online Learning Algorithms
Yunwen Lei, Lei Shi, Zheng-Chu Guo; (171):1−33, 2018.
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Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura; (172):1−38, 2018.
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auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller; (173):1−5, 2018.
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On the Stability of Feature Selection Algorithms
Sarah Nogueira, Konstantinos Sechidis, Gavin Brown; (174):1−54, 2018.
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Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard
Christopher Tosh, Sanjoy Dasgupta; (175):1−11, 2018.
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The DFS Fused Lasso: Linear-Time Denoising over General Graphs
Oscar Hernan Madrid Padilla, James Sharpnack, James G. Scott, Ryan J. Tibshirani; (176):1−36, 2018.
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Community Detection and Stochastic Block Models: Recent Developments
Emmanuel Abbe; (177):1−86, 2018.
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On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data
Rajen D. Shah, Nicolai Meinshausen; (178):1−42, 2018.
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Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Quanming Yao, James T. Kwok; (179):1−52, 2018.
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Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyv{ä}rinen, Gang Niu, Masashi Sugiyama; (180):1−47, 2018.
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To Tune or Not to Tune the Number of Trees in Random Forest
Philipp Probst, Anne-Laure Boulesteix; (181):1−18, 2018.
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Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions
Heng Lian, Zengyan Fan; (182):1−26, 2018.
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Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits
Zifan Li, Ambuj Tewari; (183):1−24, 2018.
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On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong; (184):1−24, 2018.
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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar; (185):1−52, 2018.
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Submatrix localization via message passing
Bruce Hajek, Yihong Wu, Jiaming Xu; (186):1−52, 2018.
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Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio; (187):1−30, 2018.
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Significance-based community detection in weighted networks
John Palowitch, Shankar Bhamidi, Andrew B. Nobel; (188):1−48, 2018.
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Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor
Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka; (189):1−41, 2018.
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Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
Benjamin Guedj, Bhargav Srinivasa Desikan; (190):1−5, 2018.
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KELP: a Kernel-based Learning Platform
Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Aless, ro Moschitti, Danilo Croce, Roberto Basili; (191):1−5, 2018.
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Uncovering Causality from Multivariate Hawkes Integrated Cumulants
Massil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-François Muzy, Massil Achab; (192):1−28, 2018.
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Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
Jennifer Wortman Vaughan; (193):1−46, 2018.
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Enhancing Identification of Causal Effects by Pruning
Santtu Tikka, Juha Karvanen; (194):1−23, 2018.
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Active Nearest-Neighbor Learning in Metric Spaces
Aryeh Kontorovich, Sivan Sabato, Ruth Urner; (195):1−38, 2018.
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From Predictive Methods to Missing Data Imputation: An Optimization Approach
Dimitris Bertsimas, Colin Pawlowski, Ying Daisy Zhuo; (196):1−39, 2018.
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Saturating Splines and Feature Selection
Nicholas Boyd, Trevor Hastie, Stephen Boyd, Benjamin Recht, Michael I. Jordan; (197):1−32, 2018.
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Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization
Andrei Patrascu, Ion Necoara; (198):1−42, 2018.
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Simple, Robust and Optimal Ranking from Pairwise Comparisons
Nihar B. Shah, Martin J. Wainwright; (199):1−38, 2018.
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Surprising properties of dropout in deep networks
David P. Helmbold, Philip M. Long; (200):1−28, 2018.
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Exact Learning of Lightweight Description Logic Ontologies
Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter; (201):1−63, 2018.
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Sparse Concordance-assisted Learning for Optimal Treatment Decision
Shuhan Liang, Wenbin Lu, Rui Song, Lan Wang; (202):1−26, 2018.
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Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
Junwei Lu, Mladen Kolar, Han Liu; (203):1−78, 2018.
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Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
Quan Zhang, Mingyuan Zhou; (204):1−33, 2018.
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Steering Social Activity: A Stochastic Optimal Control Point Of View
Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez; (205):1−35, 2018.
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The Search Problem in Mixture Models
Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai; (206):1−61, 2018.
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An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application
Jianqing Fan, Weichen Wang, Yiqiao Zhong; (207):1−42, 2018.
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A Tight Bound of Hard Thresholding
Jie Shen, Ping Li; (208):1−42, 2018.
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Estimation of Graphical Models through Structured Norm Minimization
Davoud Ataee Tarzanagh, George Michailidis; (209):1−48, 2018.
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Sparse Exchangeable Graphs and Their Limits via Graphon Processes
Christian Borgs, Jennifer T. Chayes, Henry Cohn, Nina Holden; (210):1−71, 2018.
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Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning
Jiyan Yang, Yin-Lam Chow, Christopher R{é}, Michael W. Mahoney; (211):1−43, 2018.
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Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
Hongzhou Lin, Julien Mairal, Zaid Harchaoui; (212):1−54, 2018.
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Gaussian Lower Bound for the Information Bottleneck Limit
Amichai Painsky, Naftali Tishby; (213):1−29, 2018.
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tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Ga\"{\i}ffas, S{ø}ren V. Poulsen; (214):1−5, 2018.
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SGDLibrary: A MATLAB library for stochastic optimization algorithms
Hiroyuki Kasai; (215):1−5, 2018.
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Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
Swapna Buccapatnam, Fang Liu, Atilla Eryilmaz, Ness B. Shroff; (216):1−34, 2018.
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Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng; (217):1−58, 2018.
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Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
Shusen Wang, Alex Gittens, Michael W. Mahoney; (218):1−50, 2018.
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Compact Convex Projections
Steffen Gr{ü}newälder; (219):1−43, 2018.
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Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
Emilija Perkovi\'c, Johannes Textor, Markus Kalisch, Marloes H. Maathuis; (220):1−62, 2018.
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Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
Zeyuan Allen-Zhu; (221):1−51, 2018.
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Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
Alon Gonen, Shai Shalev-Shwartz; (222):1−13, 2018.
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Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; (223):1−42, 2018.
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Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS)
Gunwoong Park, Garvesh Raskutti; (224):1−44, 2018.
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Improved spectral community detection in large heterogeneous networks
Hafiz TIOMOKO ALI, Romain COUILLET; (225):1−49, 2018.
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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, Daniel L Sussman; (226):1−92, 2018.
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Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
Maik Döring, László Györfi, Harro Walk; (227):1−16, 2018.
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A Theory of Learning with Corrupted Labels
Brendan van Rooyen, Robert C. Williamson; (228):1−50, 2018.
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Interactive Algorithms: Pool, Stream and Precognitive Stream
Sivan Sabato, Tom Hess; (229):1−39, 2018.
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CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takáč, Michael I. Jordan, Martin Jaggi; (230):1−49, 2018.
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Concentration inequalities for empirical processes of linear time series
Likai Chen, Wei Biao Wu; (231):1−46, 2018.
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A Cluster Elastic Net for Multivariate Regression
Bradley S. Price, Ben Sherwood; (232):1−39, 2018.
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Characteristic and Universal Tensor Product Kernels
Zoltán Szabó, Bharath K. Sriperumbudur; (233):1−29, 2018.
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Learning Certifiably Optimal Rule Lists for Categorical Data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin; (234):1−78, 2018.
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