JMLR 2017

139 papers

A Bayesian Framework for Learning Rule Sets for Interpretable Classification Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, Stanley Durrleman

A Distributed Block Coordinate Descent Method for Training L1 Regularized Linear Classifiers Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang

A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms Huishuai Zhang, Yingbin Liang, Yuejie Chi

A Robust-Equitable Measure for Feature Ranking and Selection A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang

A Spectral Algorithm for Inference in Hidden Semi-Markov Models Igor Melnyk, Arindam Banerjee

A Survey of Algorithms and Analysis for Adaptive Online Learning H. Brendan McMahan

A Survey of Preference-Based Reinforcement Learning Methods Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification Naoki Ito, Akiko Takeda, Kim-Chuan Toh

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

Achieving Optimal Misclassification Proportion in Stochastic Block Models Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou

Active-Set Methods for Submodular Minimization Problems K. S. Sesh Kumar, Francis Bach

Adaptive Randomized Dimension Reduction on Massive Data Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt

An Easy-to-Hard Learning Paradigm for Multiple Classes and Multiple Labels Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller

An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback Ohad Shamir

Analyzing Tensor Power Method Dynamics in Overcomplete Regime Animashree Anandkumar, Rong Ge, Majid Janzamin

Angle-Based Multicategory Distance-Weighted SVM Hui Sun, Bruce A. Craig, Lingsong Zhang

Approximation Vector Machines for Large-Scale Online Learning Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy

Asymptotic Behavior of Support Vector Machine for Spiked Population Model Hanwen Huang

Automatic Differentiation Variational Inference Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

Averaged Collapsed Variational Bayes Inference Katsuhiko Ishiguro, Issei Sato, Naonori Ueda

Bayesian Inference for Spatio-Temporal Spike-and-Slab Priors Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen

Bayesian Learning of Dynamic Multilayer Networks Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts

Bayesian Network Learning via Topological Order Young Woong Park, Diego Klabjan

Bayesian Tensor Regression Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson

Breaking the Curse of Dimensionality with Convex Neural Networks Francis Bach

Bridging Supervised Learning and Test-Based Co-Optimization Elena Popovici

Certifiably Optimal Low Rank Factor Analysis Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder

Classification of Time Sequences Using Graphs of Temporal Constraints Mathieu Guillame-Bert, Artur Dubrawski

Clustering from General Pairwise Observations with Applications to Time-Varying Graphs Shiau Hong Lim, Yudong Chen, Huan Xu

Clustering with Hidden Markov Model on Variable Blocks Lin Lin, Jia Li

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

Communication-Efficient Sparse Regression Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor

Community Extraction in Multilayer Networks with Heterogeneous Community Structure James D. Wilson, John Palowitch, Shankar Bhamidi, Andrew B. Nobel

Computational Limits of a Distributed Algorithm for Smoothing Spline Zuofeng Shang, Guang Cheng

Confidence Sets with Expected Sizes for Multiclass Classification Christophe Denis, Mohamed Hebiri

Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering Pietro Coretto, Christian Hennig

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding Vardan Papyan, Yaniv Romano, Michael Elad

Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas

Density Estimation in Infinite Dimensional Exponential Families Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

Differential Privacy for Bayesian Inference Through Posterior Sampling Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein

Dimension Estimation Using Random Connection Models Paulo Serra, Michel Mandjes

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

Distributed Learning with Regularized Least Squares Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou

Distributed Semi-Supervised Learning with Kernel Ridge Regression Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou

Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks Adam S. Charles, Dong Yin, Christopher J. Rozell

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang

Document Neural Autoregressive Distribution Estimation Stanislas Lauly, Yin Zheng, Alexandre Allauzen, Hugo Larochelle

Efficient Sampling from Time-Varying Log-Concave Distributions Hariharan Narayanan, Alexer Rakhlin

Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters Jacques Wainer, Gavin Cawley

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease

Faithfulness of Probability Distributions and Graphs Kayvan Sadeghi

Fisher Consistency for Prior Probability Shift Dirk Tasche

Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári

Fundamental Conditions for Low-CP-Rank Tensor Completion Morteza Ashraphijuo, Xiaodong Wang

Gap Safe Screening Rules for Sparsity Enforcing Penalties Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Generalized Conditional Gradient for Sparse Estimation Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy

Generalized SURE for Optimal Shrinkage of Singular Values in Low-Rank Matrix Denoising Jérémie Bigot, Charles Deledalle, Delphine Féral

Group Sparse Optimization via Lp,q Regularization Yaohua Hu, Chong Li, Kaiwen Meng, Jing Qin, Xiaoqi Yang

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach

Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta

Hierarchically Compositional Kernels for Scalable Nonparametric Learning Jie Chen, Haim Avron, Vikas Sindhwani

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor

Identifying a Minimal Class of Models for High--Dimensional Data Daniel Nevo, Ya'acov Ritov

Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman

Improving Variational Methods via Pairwise Linear Response Identities Jack Raymond, Federico Ricci-Tersenghi

Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen

Joint Label Inference in Networks Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy

Kernel Partial Least Squares for Stationary Data Marco Singer, Tatyana Krivobokova, Axel Munk

Knowledge Graph Completion via Complex Tensor Factorization Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, Guillaume Bouchard

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions Ricardo Silva, Shohei Shimizu

Learning Local Dependence in Ordered Data Guo Yu, Jacob Bien

Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning Jervis Pinto, Alan Fern

Learning Scalable Deep Kernels with Recurrent Structure Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network Zheng-Chu Guo, Lei Shi, Qiang Wu

Lens Depth Function and K-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis Matthäus Kleindessner, Ulrike von Luxburg

Local Algorithms for Interactive Clustering Pranjal Awasthi, Maria Florina Balcan, Konstantin Voevodski

Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions Weiwei Liu, Ivor W. Tsang

Matrix Completion with Noisy Entries and Outliers Raymond K. W. Wong, Thomas C. M. Lee

Memory Efficient Kernel Approximation Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon

Minimax Estimation of Kernel Mean Embeddings Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet

Minimax Filter: Learning to Preserve Privacy from Inference Attacks Jihun Hamm

Multiscale Strategies for Computing Optimal Transport Samuel Gerber, Mauro Maggioni

Nearly Optimal Classification for Semimetrics Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

Non-Parametric Policy Search with Limited Information Loss Herke van Hoof, Gerhard Neumann, Jan Peters

Nonparametric Risk Bounds for Time-Series Forecasting Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish

On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models Yining Wang, Adams Wei Yu, Aarti Singh

On Markov Chain Monte Carlo Methods for Tall Data Rémi Bardenet, Arnaud Doucet, Chris Holmes

On Perturbed Proximal Gradient Algorithms Yves F. Atchadé, Gersende Fort, Eric Moulines

On the Consistency of Ordinal Regression Methods Fabian Pedregosa, Francis Bach, Alexandre Gramfort

On the Equivalence Between Kernel Quadrature Rules and Random Feature Expansions Francis Bach

On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks Prakash Balachandran, Eric D. Kolaczyk, Weston D. Viles

Online Bayesian Passive-Aggressive Learning Tianlin Shi, Jun Zhu

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling Christophe Dupuy, Francis Bach

Online Learning to Rank with Top-K Feedback Sougata Chaudhuri, Ambuj Tewari

Optimal Dictionary for Least Squares Representation Mohammed Rayyan Sheriff, Debasish Chatterjee

Optimal Rates for Multi-Pass Stochastic Gradient Methods Junhong Lin, Lorenzo Rosasco

Parallel Symmetric Class Expression Learning An C. Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsland

Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth

Perishability of Data: Dynamic Pricing Under Varying-Coefficient Models Adel Javanmard

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

Poisson Random Fields for Dynamic Feature Models Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh

Preference-Based Teaching Ziyuan Gao, Christoph Ries, Hans U. Simon, Sandra Zilles

Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig

Quantifying the Informativeness of Similarity Measurements Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas

Rank Determination for Low-Rank Data Completion Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal

Reconstructing Undirected Graphs from Eigenspaces Yohann De Castro, Thibault Espinasse, Paul Rochet

Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail

Regularization and the Small-Ball Method II: Complexity Dependent Error Rates Guillaume Lecué, Shahar Mendelson

Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models Jiahe Lin, George Michailidis

Relational Reinforcement Learning for Planning with Exogenous Effects David Martínez, Guillem Alenyà, Tony Ribeiro, Katsumi Inoue, Carme Torras

Robust and Scalable Bayes via a Median of Subset Posterior Measures Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson

Robust Discriminative Clustering with Sparse Regularizers Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach

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

Second-Order Stochastic Optimization for Machine Learning in Linear Time Naman Agarwal, Brian Bullins, Elad Hazan

Sharp Oracle Inequalities for Square Root Regularization Benjamin Stucky, Sara van de Geer

Simplifying Probabilistic Expressions in Causal Inference Santtu Tikka, Juha Karvanen

Soft Margin Support Vector Classification as Buffered Probability Minimization Matthew Norton, Alexander Mafusalov, Stan Uryasev

Spectral Clustering Based on Local PCA Ery Arias-Castro, Gilad Lerman, Teng Zhang

Stability of Controllers for Gaussian Process Dynamics Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters

Stabilized Sparse Online Learning for Sparse Data Yuting Ma, Tian Zheng

Statistical and Computational Guarantees for the Baum-Welch Algorithm Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright

Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences Takashi Takenouchi, Takafumi Kanamori

Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt, Matthew D. Hoffman, David M. Blei

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization Yuchen Zhang, Lin Xiao

STORE: Sparse Tensor Response Regression and Neuroimaging Analysis Will Wei Sun, Lexin Li

Target Curricula via Selection of Minimum Feature Sets: A Case Study in Boolean Networks Shannon Fenn, Pablo Moscato

Tests of Mutual or Serial Independence of Random Vectors with Applications Martin Bilodeau, Aurélien Guetsop Nangue

The Impact of Random Models on Clustering Similarity Alexander J. Gates, Yong-Yeol Ahn

Time for a Change: A Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis Alessio Benavoli, Giorgio Corani, Janez Demšar, Marco Zaffalon

Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality Samory Kpotufe, Nakul Verma

Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques Debarghya Ghoshdastidar, Ambedkar Dukkipati

Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns Herbert Jaeger

Variational Particle Approximations Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman