JMLR 2016

236 papers

On the properties of variational approximations of Gibbs posteriors

Pierre Alquier, James Ridgway, Nicolas Chopin

Distributed Submodular Maximization

Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause

Blending Learning and Inference in Conditional Random Fields

Tamir Hazan, Alexander G. Schwing, Raquel Urtasun

An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension

Bo Peng, Lan Wang, Yichao Wu

Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses

Peter Schulam, Suchi Saria

A Characterization of Linkage-Based Hierarchical Clustering

Margareta Ackerman, Shai Ben-David

Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation

Nuaman Asbeh, Boaz Lerner

A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees

Michael Chichignoud, Johannes Lederer, Martin J. Wainwright

Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption

Chenxin Ma, Rachael Tappenden, Martin Takac

Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case

Joon Kwon, Vianney Perchet

Approximate Newton Methods for Policy Search in Markov Decision Processes

Thomas Furmston, Guy Lever, David Barber

Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling

Terrance D. Savitsky

GenSVM: A Generalized Multiclass Support Vector Machine

Gerrit J.j. Van Den Burg, Patrick J.F. Groenen

Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview

Nuaman Asbeh, Boaz Lerner

Composite Multiclass Losses

Robert C. Williamson, Elodie Vernet, Mark D. Reid

Stability and Generalization in Structured Prediction

Ben London, Bert Huang, Lise Getoor

RLScore: Regularized Least-Squares Learners

Tapio Pahikkala, Antti Airola

Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize

Huizhen Yu

On Bayes Risk Lower Bounds

Xi Chen, Adityanand Guntuboyina, Yuchen Zhang

Multi-scale Classification using Localized Spatial Depth

Subhajit Dutta, Soham Sarkar, Anil K. Ghosh

Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing

Xi Chen, Kevin Jiao, Qihang Lin

Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma

Murat A. Erdogdu

Learning Planar Ising Models

Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli

A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares

Garvesh Raskutti, Michael W. Mahoney

Neyman-Pearson Classification under High-Dimensional Settings

Anqi Zhao, Yang Feng, Lie Wang, Xin Tong

Measuring Dependence Powerfully and Equitably

Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael Mitzenmacher

Multi-Objective Markov Decision Processes for Data-Driven Decision Support

Daniel J. Lizotte, Eric B. Laber

Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition

Shusen Wang, Zhihua Zhang, Tong Zhang

Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation

Sylvain Arlot, Matthieu Lerasle

Modelling Interactions in High-dimensional Data with Backtracking

Rajen D. Shah

ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm

Ery Arias-Castro, David Mason, Bruno Pelletier

Neural Autoregressive Distribution Estimation

Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle

Bayesian Graphical Models for Multivariate Functional Data

Hongxiao Zhu, Nate Strawn, David B. Dunson

Guarding against Spurious Discoveries in High Dimensions

Jianqing Fan, Wen-Xin Zhou

Nonparametric Network Models for Link Prediction

Sinead A. Williamson

Multivariate Spearman's ρ for Aggregating Ranks Using Copulas

Justin Bedo, Cheng Soon Ong

Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles

Xiangju Qin, Padraig Cunningham, Michael Salter-Townshend

Mutual Information Based Matching for Causal Inference with Observational Data

Lei Sun, Alexander G. Nikolaev

Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications

Oren Elisha, Shai Dekel

Machine Learning in an Auction Environment

Patrick Hummel, R. Preston McAfee

Bayesian group factor analysis with structured sparsity

Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E Engelhardt

Bipartite Ranking: a Risk-Theoretic Perspective

Aditya Krishna Menon, Robert C. Williamson

Optimal Learning Rates for Localized SVMs

Mona Meister, Ingo Steinwart

Data-driven Rank Breaking for Efficient Rank Aggregation

Ashish Khetan, Sewoong Oh

On the Influence of Momentum Acceleration on Online Learning

Kun Yuan, Bicheng Ying, Ali H. Sayed

One-class classification of point patterns of extremes

Stijn Luca, David A. Clifton, Bart Vanrumste

A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes

Tobias Sutter, Arnab Ganguly, Heinz Koeppl

Classification of Imbalanced Data with a Geometric Digraph Family

Artür Manukyan, Elvan Ceyhan

A New Algorithm and Theory for Penalized Regression-based Clustering

Chong Wu, Sunghoon Kwon, Xiaotong Shen, Wei Pan

Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis

Zhirong Yang, Jukka Corander, Erkki Oja

Electronic Health Record Analysis via Deep Poisson Factor Models

Ricardo Henao, James T. Lu, Joseph E. Lucas, Jeffrey Ferranti, Lawrence Carin

The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes

Ramin Moghaddass, Cynthia Rudin, David Madigan

fastFM: A Library for Factorization Machines

Immanuel Bayer

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle

Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg

Jointly Informative Feature Selection Made Tractable by Gaussian Modeling

Leonidas Lefakis, Francois Fleuret

Consistency of Cheeger and Ratio Graph Cuts

Nicolás García Trillos, Dejan Slepcev, James von Brecht, Thomas Laurent, Xavier Bresson

Characteristic Kernels and Infinitely Divisible Distributions

Yu Nishiyama, Kenji Fukumizu

On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching

Vince Lyzinski, Keith Levin, Donniell E. Fishkind, Carey E. Priebe

The Asymptotic Performance of Linear Echo State Neural Networks

Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali

A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries

Radoslaw Adamczak

Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels

Céline Brouard, Marie Szafranski, Florence d'Alché-Buc

bandicoot: a Python Toolbox for Mobile Phone Metadata

Yves-Alexandre De Montjoye, Luc Rocher, Alex Sandy Pentland

Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes

Chiwoo Park, Jianhua Z. Huang

Online PCA with Optimal Regret

Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth

Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues

David Rohde, Matt P. Wand

Feature-Level Domain Adaptation

Wouter M. Kouw, Laurens J.P. van der Maaten, Jesse H. Krijthe, Marco Loog

mlr: Machine Learning in R

Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter

Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach

Stavros P. Adam, George D. Magoulas, Dimitrios A. Karras, Michael N. Vrahatis

Stable Graphical Models

Navodit Misra, Ercan E. Kuruoglu

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes

Yuanjia Wang, Tianle Chen, Donglin Zeng

Joint Structural Estimation of Multiple Graphical Models

Jing Ma, George Michailidis

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

Nihar B. Shah, Dengyong Zhou

Optimal Estimation of Derivatives in Nonparametric Regression

Wenlin Dai, Tiejun Tong, Marc G. Genton

Augmentable Gamma Belief Networks

Mingyuan Zhou, Yulai Cong, Bo Chen

The Teaching Dimension of Linear Learners

Ji Liu, Xiaojin Zhu

Optimal Estimation and Completion of Matrices with Biclustering Structures

Chao Gao, Yu Lu, Zongming Ma, Harrison H. Zhou

A General Framework for Constrained Bayesian Optimization using Information-based Search

José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics

Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh

Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine

Simon Odense, Roderick Edwards

Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs

Alberto N. Escalante-B., Laurenz Wiskott

Minimum Density Hyperplanes

Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis

New Perspectives on k-Support and Cluster Norms

Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits

Gergely Neu, Gabor Bartok

A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

Weijie Su, Stephen Boyd, Emmanuel J. Candès

Learning Theory for Distribution Regression

Zoltan Szabo, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton

Conditional Independencies under the Algorithmic Independence of Conditionals

Jan Lemeire

A General Framework for Consistency of Principal Component Analysis

Dan Shen, Haipeng Shen, J. S. Marron

Kernel Estimation and Model Combination in A Bandit Problem with Covariates

Wei Qian, Yuhong Yang

Megaman: Scalable Manifold Learning in Python

James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang

Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means

Twan van Laarhoven, Elena Marchiori

Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models

Jiahe Lin, Sumanta Basu, Moulinath Banerjee, George Michailidis

True Online Temporal-Difference Learning

Harm Van Seijen, A. Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton

MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions

Rina Foygel Barber, Emil Y. Sidky

Covariance-based Clustering in Multivariate and Functional Data Analysis

Francesca Ieva, Anna Maria Paganoni, Nicholas Tarabelloni

Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning

Judy Hoffman, Deepak Pathak, Eric Tzeng, Jonathan Long, Sergio Guadarrama

Sparse PCA via Covariance Thresholding

Yash Deshpande, Andrea Montanari

Multiscale Adaptive Representation of Signals: I. The Basic Framework

Cheng Tai, Weinan E

Regularized Policy Iteration with Nonparametric Function Spaces

Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data

Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

James Townsend, Niklas Koep, Sebastian Weichwald