JMLR 2020

252 papers

Expected Policy Gradients for Reinforcement Learning

Kamil Ciosek, Shimon Whiteson

Learning Causal Networks via Additive Faithfulness

Kuang-Yao Lee, Tianqi Liu, Bing Li, Hongyu Zhao

Sparse and low-rank multivariate Hawkes processes

Emmanuel Bacry, Martin Bompaire, Stephane Gaiffas, Jean-François Muzy

Ensemble Learning for Relational Data

Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi

Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory

Thomas Ricatte, Remi Gilleron, Marc Tommasi

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement

Wouter Kool, Herke van Hoof, Max Welling

pyts: A Python Package for Time Series Classification

Johann Faouzi, Hicham Janati

A Convex Parametrization of a New Class of Universal Kernel Functions

Brendon K. Colbert, Matthew M. Peet

Dynamical Systems as Temporal Feature Spaces

Peter Tino

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

Puyudi Yang, Jianbo Chen, Cho-jui Hsieh, Jane-Ling Wang, Michael I. Jordan

Branch and Bound for Piecewise Linear Neural Network Verification

Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H.S. Torr, Pushmeet Kohli

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables

Rune Christiansen, Jonas Peters

Optimal Bipartite Network Clustering

Zhixin Zhou, Arash A. Amini

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang

Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification

Leo L. Duan

Causal Discovery Toolbox: Uncovering causal relationships in Python

Diviyan Kalainathan, Olivier Goudet, Ritik Dutta

Noise Accumulation in High Dimensional Classification and Total Signal Index

Miriam R. Elman, Jessica Minnier, Xiaohui Chang, Dongseok Choi

Learning with Fenchel-Young losses

Mathieu Blondel, André F.T. Martins, Vlad Niculae

Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent

Dominic Richards, Patrick Rebeschini

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent

Huan Li, Zhouchen Lin

Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping

Mihai Cucuringu, Hemant Tyagi

Generalized Nonbacktracking Bounds on the Influence

Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee

Tensor Train Decomposition on TensorFlow (T3F)

Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets

The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response

Xin Zhang, Qing Mai, Hui Zou

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms

Nicolás García Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso

A New Class of Time Dependent Latent Factor Models with Applications

Sinead A. Williamson, Michael Minyi Zhang, Paul Damien

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes

Anders Ellern Bilgrau, Carel F.W. Peeters, Poul Svante Eriksen, Martin Boegsted, Wessel N. van Wieringen

Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models

Ivona Bezáková, Antonio Blanca, Zongchen Chen, Daniel Stefankovic, Eric Vigoda

Distributed Feature Screening via Componentwise Debiasing

Xingxiang Li, Runze Li, Zhiming Xia, Chen Xu

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Jian Guo, He He, Tong He, Leonard Lausen, Mu Li

A Unified Framework for Structured Graph Learning via Spectral Constraints

Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Junhong Lin, Volkan Cevher

High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix

Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong

Connecting Spectral Clustering to Maximum Margins and Level Sets

David P. Hofmeyr

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data

Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran

Practical Locally Private Heavy Hitters

Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning

Ery Arias-Castro, Adel Javanmard, Bruno Pelletier

On lp-Support Vector Machines and Multidimensional Kernels

Victor Blanco, Justo Puerto, Antonio M. Rodriguez-Chia

Generalized probabilistic principal component analysis of correlated data

Mengyang Gu, Weining Shen

Neyman-Pearson classification: parametrics and sample size requirement

Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information

T. Tony Cai, Tengyuan Liang, Alexander Rakhlin

Online Sufficient Dimension Reduction Through Sliced Inverse Regression

Zhanrui Cai, Runze Li, Liping Zhu

On Mahalanobis Distance in Functional Settings

José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas

DESlib: A Dynamic ensemble selection library in Python

Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti

Target Propagation in Recurrent Neural Networks

Nikolay Manchev, Michael Spratling

Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms

Anna Little, Mauro Maggioni, James M. Murphy

Lower Bounds for Parallel and Randomized Convex Optimization

Jelena Diakonikolas, Cristóbal Guzmán

Universal Latent Space Model Fitting for Large Networks with Edge Covariates

Zhuang Ma, Zongming Ma, Hongsong Yuan

A Model of Fake Data in Data-driven Analysis

Xiaofan Li, Andrew B. Whinston

A Statistical Learning Approach to Modal Regression

Yunlong Feng, Jun Fan, Johan A.K. Suykens

A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints

Hao Yu, Michael J. Neely