JMLR 2017

149 papers

Community Extraction in Multilayer Networks with Heterogeneous Community Structure

James D. Wilson, John Palowitch, Shankar Bhamidi, Andrew B. Nobel

Faithfulness of Probability Distributions and Graphs

Kayvan Sadeghi

Matrix Completion with Noisy Entries and Outliers

Raymond K. W. Wong, Thomas C. M. Lee

Regularization and the small-ball method II: complexity dependent error rates

Guillaume Lecue, Shahar Mendelson

Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities

Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári

Generalized Conditional Gradient for Sparse Estimation

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models

Yining Wang, Adams Wei Yu, Aarti Singh

Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering

Pietro Coretto, Christian Hennig

A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms

Huishuai Zhang, Yingbin Liang, Yuejie Chi

Adaptive Randomized Dimension Reduction on Massive Data

Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt

Bayesian Inference for Spatio-temporal Spike-and-Slab Priors

Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen

Dimension Estimation Using Random Connection Models

Paulo Serra, Michel Mandjes

Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising

Jérémie Bigot, Charles Deledalle, Delphine Féral

A Survey of Preference-Based Reinforcement Learning Methods

Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz

STORE: Sparse Tensor Response Regression and Neuroimaging Analysis

Will Wei Sun, Lexin Li

Stochastic Gradient Descent as Approximate Bayesian Inference

Stephan Mandt, Matthew D. Hoffman, David M. Blei

A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations

Jean-Baptiste SCHIRATTI, Stéphanie ALLASSONNIERE, Olivier Colliot, Stanley DURRLEMAN

Active-set Methods for Submodular Minimization Problems

K. S. Sesh Kumar, Francis Bach

Stabilized Sparse Online Learning for Sparse Data

Yuting Ma, Tian Zheng

Knowledge Graph Completion via Complex Tensor Factorization

Théo Trouillon, Christopher R. Dance, Eric Gaussier, Johannes Welbl, Sebastian Riedel

Minimax Filter: Learning to Preserve Privacy from Inference Attacks

Jihun Hamm

Gap Safe Screening Rules for Sparsity Enforcing Penalties

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Poisson Random Fields for Dynamic Feature Models

Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling

Christophe Dupuy, Francis Bach

Statistical and Computational Guarantees for the Baum-Welch Algorithm

Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright

Robust and Scalable Bayes via a Median of Subset Posterior Measures

Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson

Kernel Partial Least Squares for Stationary Data

Marco Singer, Tatyana Krivobokova, Axel Munk

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement

Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang

Classification of Time Sequences using Graphs of Temporal Constraints

Mathieu Guillame-Bert, Artur Dubrawski

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions

Ricardo Silva, Shohei Shimizu

Probabilistic Line Searches for Stochastic Optimization

Maren Mahsereci, Philipp Hennig

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network

Zheng-Chu Guo, Lei Shi, Qiang Wu

Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models

Jiahe Lin, George Michailidis

Second-Order Stochastic Optimization for Machine Learning in Linear Time

Naman Agarwal, Brian Bullins, Elad Hazan

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization

Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang

Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks

Shannon Fenn, Pablo Moscato

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

Approximation Vector Machines for Large-scale Online Learning

Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

Clustering with Hidden Markov Model on Variable Blocks

Lin Lin, Jia Li

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor

Computational Limits of A Distributed Algorithm for Smoothing Spline

Zuofeng Shang, Guang Cheng

Optimal Dictionary for Least Squares Representation

Mohammed Rayyan Sheriff, Debasish Chatterjee

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server

Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan

Accelerating Stochastic Composition Optimization

Mengdi Wang, Ji Liu, Ethan X. Fang

A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

Thang D. Bui, Josiah Yan, Richard E. Turner

Online Learning to Rank with Top-k Feedback

Sougata Chaudhuri, Ambuj Tewari

Confidence Sets with Expected Sizes for Multiclass Classification

Christophe Denis, Mohamed Hebiri

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach

Stability of Controllers for Gaussian Process Dynamics

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-tuong, Jan Peters

Bayesian Network Learning via Topological Order

Young Woong Park, Diego Klabjan

Rank Determination for Low-Rank Data Completion

Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal

Optimal Rates for Multi-pass Stochastic Gradient Methods

Junhong Lin, Lorenzo Rosasco

openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit

Maximilian Schmitt, Björn Schuller

Fisher Consistency for Prior Probability Shift

Dirk Tasche

An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels

Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller

Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks

Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman

Distributed Learning with Regularized Least Squares

Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou

A distributed block coordinate descent method for training l1 regularized linear classifiers

Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan

A survey of Algorithms and Analysis for Adaptive Online Learning

H. Brendan McMahan

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, João V. Messias

Hierarchical Clustering via Spreading Metrics

Aurko Roy, Sebastian Pokutta

The Impact of Random Models on Clustering Similarity

Alexander J. Gates, Yong-Yeol Ahn

Minimax Estimation of Kernel Mean Embeddings

Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet

Angle-based Multicategory Distance-weighted SVM

Hui Sun, Bruce A. Craig, Lingsong Zhang

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

Yuchen Zhang, Lin Xiao

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

Vardan Papyan, Yaniv Romano, Michael Elad

Learning Scalable Deep Kernels with Recurrent Structure

Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing

Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions

Weiwei Liu, Ivor W. Tsang

Robust Discriminative Clustering with Sparse Regularizers

Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach

Bayesian Tensor Regression

Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson

Relational Reinforcement Learning for Planning with Exogenous Effects

David Martinez, Guillem Alenyà, Tony Ribeiro, Katsumi Inoue, Carme Torras

Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis

Alessio Benavoli, Giorgio Corani, Janez Demsar, Marco Zaffalon

Quantifying the Informativeness of Similarity Measurements

Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas

Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements

ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail

Tests of Mutual or Serial Independence of Random Vectors with Applications

Martin Bilodeau, Aurélien Guetsop Nangue

Non-parametric Policy Search with Limited Information Loss

Herke van Hoof, Gerhard Neumann, Jan Peters

Multiscale Strategies for Computing Optimal Transport

Samuel Gerber, Mauro Maggioni

A Robust-Equitable Measure for Feature Ranking and Selection

A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang

A Bayesian Framework for Learning Rule Sets for Interpretable Classification

Tong Wang, Cynthia Rudin, Finale Doshi-velez, Yimin Liu, Erica Klampfl

Variational Particle Approximations

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

Soft Margin Support Vector Classification as Buffered Probability Minimization

Matthew Norton, Alexander Mafusalov, Stan Uryasev

Sharp Oracle Inequalities for Square Root Regularization

Benjamin Stucky, Sara van de Geer

Hierarchically Compositional Kernels for Scalable Nonparametric Learning

Jie Chen, Haim Avron, Vikas Sindhwani

Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning

Jervis Pinto, Alan Fern

Parallel Symmetric Class Expression Learning

An C. Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsland

Fundamental Conditions for Low-CP-Rank Tensor Completion

Morteza Ashraphijuo, Xiaodong Wang

Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA

Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas

On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks

Prakash Balachandran, Eric D. Kolaczyk, Weston D. Viles

Achieving Optimal Misclassification Proportion in Stochastic Block Models

Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou

Joint Label Inference in Networks

Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy

Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis

Matthäus Kleindessner, Ulrike Von Luxburg

Density Estimation in Infinite Dimensional Exponential Families

Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences

Takashi Takenouchi, Takafumi Kanamori

On the Consistency of Ordinal Regression Methods

Fabian Pedregosa, Francis Bach, Alexandre Gramfort

Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect

Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar

Perishability of Data: Dynamic Pricing under Varying-Coefficient Models

Adel Javanmard

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

Ohad Shamir

Reconstructing Undirected Graphs from Eigenspaces

Yohann De Castro, Thibault Espinasse, Paul Rochet

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques

Debarghya Ghoshdastidar, Ambedkar Dukkipati