JMLR 2019

184 papers

Differentiable Game Mechanics

Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster

On the optimality of the Hedge algorithm in the stochastic regime

Jaouad Mourtada, Stephane Gaiffas

SMART: An Open Source Data Labeling Platform for Supervised Learning

Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards

Tight Lower Bounds on the VC-dimension of Geometric Set Systems

Mónika Csikós, Nabil H. Mustafa, Andrey Kupavskii

Learning to Match via Inverse Optimal Transport

Ruilin Li, Xiaojing Ye, Haomin Zhou, Hongyuan Zha

Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions

Afonso Fernandes Vaz, Rafael Izbicki, Rafael Bassi Stern

Prediction Risk for the Horseshoe Regression

Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon Willard

Nonuniformity of P-values Can Occur Early in Diverging Dimensions

Yingying Fan, Emre Demirkaya, Jinchi Lv

Generalized Score Matching for Non-Negative Data

Shiqing Yu, Mathias Drton, Ali Shojaie

Fairness Constraints: A Flexible Approach for Fair Classification

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi

Deep Optimal Stopping

Sebastian Becker, Patrick Cheridito, Arnulf Jentzen

Analysis of Langevin Monte Carlo via Convex Optimization

Alain Durmus, Szymon Majewski, Błażej Miasojedow

Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning

Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin

Lazifying Conditional Gradient Algorithms

Gábor Braun, Sebastian Pokutta, Daniel Zink

Semi-Analytic Resampling in Lasso

Tomoyuki Obuchi, Yoshiyuki Kabashima

On Consistent Vertex Nomination Schemes

Vince Lyzinski, Keith Levin, Carey E. Priebe

Variance-based Regularization with Convex Objectives

John Duchi, Hongseok Namkoong

Learnability of Solutions to Conjunctive Queries

Hubie Chen, Matthew Valeriote

Proximal Distance Algorithms: Theory and Practice

Kevin L. Keys, Hua Zhou, Kenneth Lange

Active Learning for Cost-Sensitive Classification

Akshay Krishnamurthy, Alekh Agarwal, Tzu-kuo Huang, Hal Daume III, John Langford

A Representer Theorem for Deep Kernel Learning

Bastian Bohn, Michael Griebel, Christian Rieger

Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks

Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian

Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing

Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh

The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising

Dong Xia, Fan Zhou

Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression

Wenwu Wang, Ping Yu, Lu Lin, Tiejun Tong

Kernel Approximation Methods for Speech Recognition

Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu

The Common-directions Method for Regularized Empirical Risk Minimization

Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin

Multi-class Heterogeneous Domain Adaptation

Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan

Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices

zengfeng Huang

Neural Architecture Search: A Survey

Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

Deep Reinforcement Learning for Swarm Systems

Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann

Tunability: Importance of Hyperparameters of Machine Learning Algorithms

Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl

Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems

Sondre Glimsdal, Ole-Christoffer Granmo

Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures

Gregor Pirs, Erik Strumbelj

No-Regret Bayesian Optimization with Unknown Hyperparameters

Felix Berkenkamp, Angela P. Schoellig, Andreas Krause

Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots

Akshara Rai, Rika Antonova, Franziska Meier, Christopher G. Atkeson

Efficient augmentation and relaxation learning for individualized treatment rules using observational data

Ying-Qi Zhao, Eric B. Laber, Yang Ning, Sumona Saha, Bruce E. Sands

Analysis of spectral clustering algorithms for community detection: the general bipartite setting

Zhixin Zhou, Arash A.Amini

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping

Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou

Robust Frequent Directions with Application in Online Learning

Luo Luo, Cheng Chen, Zhihua Zhang, Wu-jun Li, Tong Zhang

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang

DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization

Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen

Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations

Jialei Wang, Tong Zhang

Decontamination of Mutual Contamination Models

Julian Katz-Samuels, Gilles Blanchard, Clayton Scott

Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations

Qianxiao Li, Cheng Tai, Weinan E

A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication

Miles E. Lopes, Shusen Wang, Michael W. Mahoney

Approximation Hardness for A Class of Sparse Optimization Problems

Yichen Chen, Yinyu Ye, Mengdi Wang

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

Tyler Maunu, Teng Zhang, Gilad Lerman

A New Approach to Laplacian Solvers and Flow Problems

Patrick Rebeschini, sekhar tatikonda

Optimal Policies for Observing Time Series and Related Restless Bandit Problems

Christopher R. Dance, Tomi Silander

Matched Bipartite Block Model with Covariates

Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li

The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient

Roei Gelbhart, Ran El-Yaniv

NetSDM: Semantic Data Mining with Network Analysis

Jan Kralj, Marko Robnik-Sikonja, Nada Lavrač

Kernels for Sequentially Ordered Data

Franz J. Király, Harald Oberhauser

Exact Clustering of Weighted Graphs via Semidefinite Programming

Aleksis Pirinen, Brendan Ames

Iterated Learning in Dynamic Social Networks

Bernard Chazelle, Chu Wang

Pyro: Deep Universal Probabilistic Programming

Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan

Monotone Learning with Rectified Wire Networks

Veit Elser, Dan Schmidt, Jonathan Yedidia

TensorLy: Tensor Learning in Python

Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic

Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations

Alberto Bietti, Julien Mairal

Joint PLDA for Simultaneous Modeling of Two Factors

Luciana Ferrer, Mitchell McLaren

Determining the Number of Latent Factors in Statistical Multi-Relational Learning

Chengchun Shi, Wenbin Lu, Rui Song

Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics

Yanning Shen, Tianyi Chen, Georgios B. Giannakis

Spectrum Estimation from a Few Entries

Ashish Khetan, Sewoong Oh

Accelerated Alternating Projections for Robust Principal Component Analysis

HanQin Cai, Jian-Feng CAI, Ke Wei

spark-crowd: A Spark Package for Learning from Crowdsourced Big Data

Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez

Multiplicative local linear hazard estimation and best one-sided cross-validation

Maria Luz Gámiz, María Dolores Martínez-Miranda, Jens Perch Nielsen

Delay and Cooperation in Nonstochastic Bandits

Nicolò Cesa-bianchi, Claudio Gentile, Yishay Mansour

Smooth neighborhood recommender systems

Ben Dai, Junhui Wang, Xiaotong Shen, Annie Qu

Automated Scalable Bayesian Inference via Hilbert Coresets

Trevor Campbell, Tamara Broderick

Approximations of the Restless Bandit Problem

Steffen Grünewälder, Azadeh Khaleghi

Train and Test Tightness of LP Relaxations in Structured Prediction

Ofer Meshi, Ben London, Adrian Weller, David Sontag

Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds

Shusen Wang, Alex Gittens, Michael W. Mahoney

An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory

Mehmet Eren Ahsen, Mathukumalli Vidyasagar

Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions

Salar Fattahi, Somayeh Sojoudi

Dynamic Pricing in High-dimensions

Adel Javanmard, Hamid Nazerzadeh

Forward-Backward Selection with Early Dropping

Giorgos Borboudakis, Ioannis Tsamardinos

Scalable Approximations for Generalized Linear Problems

Murat Erdogdu, Mohsen Bayati, Lee H. Dicker

scikit-multilearn: A Python library for Multi-Label Classification

Piotr Szymański, Tomasz Kajdanowicz

Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression

Han Chen, Garvesh Raskutti, Ming Yuan

Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations

Clément Bouttier, Ioana Gavra

Parsimonious Online Learning with Kernels via Sparse Projections in Function Space

Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro

Transport Analysis of Infinitely Deep Neural Network

Sho Sonoda, Noboru Murata

Adaptation Based on Generalized Discrepancy

Corinna Cortes, Mehryar Mohri, Andres Munoz Medina