JMLR 2016

236 papers

Synergy of Monotonic Rules

Vladimir Vapnik, Rauf Izmailov

Refined Error Bounds for Several Learning Algorithms

Steve Hanneke

Adjusting for Chance Clustering Comparison Measures

Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders

Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision

Byron C. Wallace, Joël Kuiper, Aakash Sharma, Mingxi (Brian) Zhu, Iain J. Marshall

String and Membrane Gaussian Processes

Yves-Laurent Kom Samo, Stephen J. Roberts

A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty

Ashwini Maurya

An Online Convex Optimization Approach to Blackwell's Approachability

Nahum Shimkin

Multiple-Instance Learning from Distributions

Gary Doran, Soumya Ray

Dual Control for Approximate Bayesian Reinforcement Learning

Edgar D. Klenske, Philipp Hennig

On Lower and Upper Bounds in Smooth and Strongly Convex Optimization

Yossi Arjevani, Shai Shalev-shwartz, Ohad Shamir

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Michael U. Gutmann, Jukka Corander

Bootstrap-Based Regularization for Low-Rank Matrix Estimation

Julie Josse, Stefan Wager

The Constrained Dantzig Selector with Enhanced Consistency

Yinfei Kong, Zemin Zheng, Jinchi Lv

Multiple Output Regression with Latent Noise

Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen

Variational Dependent Multi-output Gaussian Process Dynamical Systems

Jing Zhao, Shiliang Sun

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney

Volumetric Spanners: An Efficient Exploration Basis for Learning

Elad Hazan, Zohar Karnin

Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling

Chengwei Su, Mark E. Borsuk

Revisiting the Nyström Method for Improved Large-scale Machine Learning

Alex Gittens, Michael W. Mahoney

A Network That Learns Strassen Multiplication

Veit Elser

Equivalence of Graphical Lasso and Thresholding for Sparse Graphs

Somayeh Sojoudi

The LRP Toolbox for Artificial Neural Networks

Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

Fused Lasso Approach in Regression Coefficients Clustering -- Learning Parameter Heterogeneity in Data Integration

Lu Tang, Peter X.K. Song

Decrypting “Cryptogenic” Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients

Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniekcy, Orrin Devinsky

Minimax Adaptive Estimation of Nonparametric Hidden Markov Models

Yohann De Castro, Elisabeth Gassiat, Claire Lacour

Are Random Forests Truly the Best Classifiers?

Michael Wainberg, Babak Alipanahi, Brendan J. Frey

Monotonic Calibrated Interpolated Look-Up Tables

Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini

Distribution-Matching Embedding for Visual Domain Adaptation

Mahsa Baktashmotlagh, Mehrtash Harandi, Mathieu Salzmann

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation

Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao

Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling

Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Randy Katz

Trend Filtering on Graphs

Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani

e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem

Marcela Zuluaga, Andreas Krause, Markus Püschel

Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models

Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther

Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing

Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan

Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling

Ru He, Jin Tian, Huaiqing Wu

Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models

Zijian Guo, Dylan S. Small

How to Center Deep Boltzmann Machines

Jan Melchior, Asja Fischer, Laurenz Wiskott

Learning Taxonomy Adaptation in Large-scale Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard

Cells in Multidimensional Recurrent Neural Networks

Gundram Leifert, Tobias Strauß, Tobias Grüning, Welf Wustlich, Roger Labahn

Integrated Common Sense Learning and Planning in POMDPs

Brendan Juba

JCLAL: A Java Framework for Active Learning

Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura

Convex Regression with Interpretable Sharp Partitions

Ashley Petersen, Noah Simon, Daniela Witten

Hierarchical Relative Entropy Policy Search

Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters

Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices

Dan Yang, Zongming Ma, Andreas Buja

Rounding-based Moves for Semi-Metric Labeling

M. Pawan Kumar, Puneet K. Dokania

Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm

Manuel Gomez-Rodriguez, Le Song, Hadi Daneshm, Bernhard Schölkopf

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes

Xin Guo, Jun Fan, Ding-Xuan Zhou

Spectral Ranking using Seriation

Fajwel Fogel, Alexandre D'aspremont, Milan Vojnovic

L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs

Matey Neykov, Jun S. Liu, Tianxi Cai

LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems

Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan

Structure-Leveraged Methods in Breast Cancer Risk Prediction

Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu

Lenient Learning in Independent-Learner Stochastic Cooperative Games

Ermo Wei, Sean Luke

CVXPY: A Python-Embedded Modeling Language for Convex Optimization

Steven Diamond, Stephen Boyd

Model-free Variable Selection in Reproducing Kernel Hilbert Space

Lei Yang, shaogao lv, Junhui Wang

The Benefit of Multitask Representation Learning

Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes

Multiplicative Multitask Feature Learning

Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song

Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach

Jenna Wiens, John Guttag, Eric Horvitz

Latent Space Inference of Internet-Scale Networks

Qirong Ho, Junming Yin, Eric P. Xing

Iterative Regularization for Learning with Convex Loss Functions

Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou

Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics

Stéphan Clémençon, Igor Colin, Aurélien Bellet

Distributed Coordinate Descent Method for Learning with Big Data

Peter Richtarik, Martin Takac

Learning Algorithms for Second-Price Auctions with Reserve

Mehryar Mohri, Andres Munoz Medina

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning

Richard S. Sutton, A. Rupam Mahmood, Martha White

Challenges in multimodal gesture recognition

Sergio Escalera, Vassilis Athitsos, Isabelle Guyon

Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums

P.-L. Giscard, Z. Choo, S. J. Thwaite, D. Jaksch

On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting

Matey Neykov, Jun S. Liu, Tianxi Cai

Compressed Gaussian Process for Manifold Regression

Rajarshi Guhaniyogi, David B. Dunson

An Information-Theoretic Analysis of Thompson Sampling

Daniel Russo, Benjamin Van Roy

Practical Kernel-Based Reinforcement Learning

André M.S. Barreto, Doina Precup, Joelle Pineau

Bayesian Policy Gradient and Actor-Critic Algorithms

Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

Jure Zbontar, Yann LeCun

StructED: Risk Minimization in Structured Prediction

Yossi Adi, Joseph Keshet

Convergence of an Alternating Maximization Procedure

Andreas Andresen, Vladimir Spokoiny

The Statistical Performance of Collaborative Inference

Gérard Biau, Kevin Bleakley, Benoît Cadre

DSA: Decentralized Double Stochastic Averaging Gradient Algorithm

Aryan Mokhtari, Alejandro Ribeiro

Probabilistic Low-Rank Matrix Completion from Quantized Measurements

Sonia A. Bhaskar

Domain-Adversarial Training of Neural Networks

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran

Structure Discovery in Bayesian Networks by Sampling Partial Orders

Teppo Niinimäki, Pekka Parviainen, Mikko Koivisto

Causal Inference through a Witness Protection Program

Ricardo Silva, Robin Evans

Adaptive Lasso and group-Lasso for functional Poisson regression

Stéphane Ivanoff, Franck Picard, Vincent Rivoirard

Estimating Causal Structure Using Conditional DAG Models

Chris. J. Oates, Jim Q. Smith, Sach Mukherjee

Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares

Mert Pilanci, Martin J. Wainwright

Subspace Learning with Partial Information

Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-shwartz

Differentially Private Data Releasing for Smooth Queries

Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang

Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms

Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions

Shusen Wang, Luo Luo, Zhihua Zhang

Kernel Mean Shrinkage Estimators

Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf

Large Scale Online Kernel Learning

Jing LU, Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Zhi-Yong Liu

Addressing Environment Non-Stationarity by Repeating Q-learning Updates

Sherief Abdallah, Michael Kaisers

A Unified View on Multi-class Support Vector Classification

Urun Dogan, Tobias Glasmachers, Christian Igel

Scalable Learning of Bayesian Network Classifiers

Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar A. Zaidi

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

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

BayesPy: Variational Bayesian Inference in Python

Jaakko Luttinen

On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint

Chong Zhang, Yufeng Liu, Yichao Wu

End-to-End Training of Deep Visuomotor Policies

Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel

The Optimal Sample Complexity of PAC Learning

Steve Hanneke

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks

Shiliang Zhang, Hui Jiang, Lirong Dai