JMLR 2021

290 papers

Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs

Tomer Galanti, Sagie Benaim, Lior Wolf

Bayesian Text Classification and Summarization via A Class-Specified Topic Model

Feifei Wang, Junni L. Zhang, Yichao Li, Ke Deng, Jun S. Liu

Edge Sampling Using Local Network Information

Can M. Le

On Solving Probabilistic Linear Diophantine Equations

Patrick Kreitzberg, Oliver Serang

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

Gradient Methods Never Overfit On Separable Data

Ohad Shamir

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference

Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen

Statistical Query Lower Bounds for Tensor PCA

Rishabh Dudeja, Daniel Hsu

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh

Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction

Maxime Cauchois, Suyash Gupta, John C. Duchi

Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA

zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang

Is SGD a Bayesian sampler? Well, almost

Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis

POT: Python Optimal Transport

Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon

ChainerRL: A Deep Reinforcement Learning Library

Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa

Analyzing the discrepancy principle for kernelized spectral filter learning algorithms

Alain Celisse, Martin Wahl

Attention is Turing-Complete

Jorge Pérez, Pablo Barceló, Javier Marinković

Kernel Operations on the GPU, with Autodiff, without Memory Overflows

Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain DURIF

Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives

Michael Muehlebach, Michael I. Jordan

Prediction against a limited adversary

Erhan Bayraktar, Ibrahim Ekren, Xin Zhang

Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit

Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang

Testing Conditional Independence via Quantile Regression Based Partial Copulas

Lasse Petersen, Niels Richard Hansen

Determining the Number of Communities in Degree-corrected Stochastic Block Models

Shujie Ma, Liangjun Su, Yichong Zhang

Path Length Bounds for Gradient Descent and Flow

Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas

A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family

Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun

Approximate Newton Methods

Haishan Ye, Luo Luo, Zhihua Zhang

Dynamic Tensor Recommender Systems

Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu

Sparse Tensor Additive Regression

Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang

Geometric structure of graph Laplacian embeddings

Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini

How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information

Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre

Stochastic Proximal AUC Maximization

Yunwen Lei, Yiming Ying

A Distributed Method for Fitting Laplacian Regularized Stratified Models

Jonathan Tuck, Shane Barratt, Stephen Boyd

Predictive Learning on Hidden Tree-Structured Ising Models

Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach

Zhe Fei, Yi Li

Normalizing Flows for Probabilistic Modeling and Inference

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

Incorporating Unlabeled Data into Distributionally Robust Learning

Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

Minjie Wang, Genevera I. Allen

GemBag: Group Estimation of Multiple Bayesian Graphical Models

Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang

Subspace Clustering through Sub-Clusters

Weiwei Li, Jan Hannig, Sayan Mukherjee

Sparse and Smooth Signal Estimation: Convexification of L0-Formulations

Alper Atamturk, Andres Gomez, Shaoning Han

Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks

Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li

Structure Learning of Undirected Graphical Models for Count Data

Nguyen Thi Kim Hue, Monica Chiogna

From Low Probability to High Confidence in Stochastic Convex Optimization

Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang

Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression

Behzad Azmi, Dante Kalise, Karl Kunisch

Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory

Soon Hoe Lim

Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates

Tony Cai, Hongzhe Li, Rong Ma

RaSE: Random Subspace Ensemble Classification

Ye Tian, Yang Feng

Wasserstein barycenters can be computed in polynomial time in fixed dimension

Jason M Altschuler, Enric Boix-Adsera

Banach Space Representer Theorems for Neural Networks and Ridge Splines

Rahul Parhi, Robert D. Nowak

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction

Henning Lange, Steven L. Brunton, J. Nathan Kutz

Residual Energy-Based Models for Text

Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'aurelio Ranzato

giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi

Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures

Umit Köse, Andrzej Ruszczyński

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables

Zhao Tang Luo, Huiyan Sang, Bani Mallick

Multi-class Gaussian Process Classification with Noisy Inputs

Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán, Daniel Hernández-lobato

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation

Melkior Ornik, Ufuk Topcu

Neighborhood Structure Assisted Non-negative Matrix Factorization and Its Application in Unsupervised Point-wise Anomaly Detection

Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, Yu Ding

Asynchronous Online Testing of Multiple Hypotheses

Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories

Fei Lu, Mauro Maggioni, Sui Tang

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

Oliver Kroemer, Scott Niekum, George Konidaris

Single and Multiple Change-Point Detection with Differential Privacy

Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings

Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits

Julian Zimmert, Yevgeny Seldin

Inference In High-dimensional Single-Index Models Under Symmetric Designs

Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov

Finite Time LTI System Identification

Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions

Yunwen Lei, Ting Hu, Ke Tang

Entangled Kernels - Beyond Separability

Riikka Huusari, Hachem Kadri

A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems

Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone

When random initializations help: a study of variational inference for community detection

Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee

A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters

Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh

Aggregated Hold-Out

Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle

Ranking and synchronization from pairwise measurements via SVD

Alexandre D'aspremont, Mihai Cucuringu, Hemant Tyagi

A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective

Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang

Continuous Time Analysis of Momentum Methods

Nikola B. Kovachki, Andrew M. Stuart

Pykg2vec: A Python Library for Knowledge Graph Embedding

Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque

Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples

Jagdeep Singh Bhatia

On Multi-Armed Bandit Designs for Dose-Finding Trials

Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere

Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors

Di Xiao, Yuan Ke, Runze Li

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit

Shenglong Zhou, Naihua Xiu, Hou-Duo Qi

Unfolding-Model-Based Visualization: Theory, Method and Applications

Yunxiao Chen, Zhiliang Ying, Haoran Zhang

Mixing Time of Metropolis-Hastings for Bayesian Community Detection

Bumeng Zhuo, Chao Gao

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm

Defeng Sun, Kim-Chuan Toh, Yancheng Yuan

A Unified Framework for Random Forest Prediction Error Estimation

Benjamin Lu, Johanna Hardin

Preference-based Online Learning with Dueling Bandits: A Survey

Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier

Consistent estimation of small masses in feature sampling

Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro

The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models

Carlos A. Gomez-Uribe, Brian Karrer

An Empirical Study of Bayesian Optimization: Acquisition Versus Partition

Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia

Regulating Greed Over Time in Multi-Armed Bandits

Stefano Tracà, Cynthia Rudin, Weiyu Yan

Domain Generalization by Marginal Transfer Learning

Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests

Krishnakumar Balasubramanian, Tong Li, Ming Yuan