JMLR 2020

252 papers

Dual Iterative Hard Thresholding

Xiao-Tong Yuan, Bo Liu, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature

Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne

The Kalai-Smorodinsky solution for many-objective Bayesian optimization

Mickael Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal

Distributionally Ambiguous Optimization for Batch Bayesian Optimization

Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart

Local Causal Network Learning for Finding Pairs of Total and Direct Effects

Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng, Chunchen Liu

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Junhong Lin, Volkan Cevher

New Insights and Perspectives on the Natural Gradient Method

James Martens

Orlicz Random Fourier Features

Linda Chamakh, Emmanuel Gobet, Zoltan Szabo

Empirical Priors for Prediction in Sparse High-dimensional Linear Regression

Ryan Martin, Yiqi Tang

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints

Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang

Nesterov's Acceleration for Approximate Newton

Haishan Ye, Luo Luo, Zhihua Zhang

Importance Sampling Techniques for Policy Optimization

Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks

Amir R. Asadi, Emmanuel Abbe

metric-learn: Metric Learning Algorithms in Python

William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions

Benjamin Fehrman, Benjamin Gess, Arnulf Jentzen

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks

Owen Marschall, Kyunghyun Cho, Cristina Savin

Probabilistic Learning on Graphs via Contextual Architectures

Davide Bacciu, Federico Errica, Alessio Micheli

Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers

Yao Ma, Alex Olshevsky, Csaba Szepesvári, Venkatesh Saligrama

Monte Carlo Gradient Estimation in Machine Learning

Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

Convergence of Sparse Variational Inference in Gaussian Processes Regression

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models

Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind

A General System of Differential Equations to Model First-Order Adaptive Algorithms

Andre Belotto da Silva, Maxime Gazeau

A Regularization-Based Adaptive Test for High-Dimensional GLMs

Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan

Apache Mahout: Machine Learning on Distributed Dataflow Systems

Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman

Distributed Minimum Error Entropy Algorithms

Xin Guo, Ting Hu, Qiang Wu

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization

Rad Niazadeh, Tim Roughgarden, Joshua R. Wang

Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination

Hang Yu, Songwei Wu, Luyin Xin, Justin Dauwels

Tensor Regression Networks

Jean Kossaifi, Zachary C. Lipton, Arinbjörn Kolbeinsson, Aran Khanna, Tommaso Furlanello

Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering

Israel A. Almodóvar-Rivera, Ranjan Maitra

Agnostic Estimation for Phase Retrieval

Matey Neykov, Zhaoran Wang, Han Liu

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning

Aryan Mokhtari, Alec Koppel, Martin Takac, Alejandro Ribeiro

Bayesian Closed Surface Fitting Through Tensor Products

Olivier Binette, Debdeep Pati, David B. Dunson

Tslearn, A Machine Learning Toolkit for Time Series Data

Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz

Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models

Jiahe Lin, George Michailidis

GluonTS: Probabilistic and Neural Time Series Modeling in Python

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus

Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space

Elisabeth Gassiat, Sylvain Le Corff, Luc Lehéricy

NEVAE: A Deep Generative Model for Molecular Graphs

Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Chattaraj

Prediction regions through Inverse Regression

Emilie Devijver, Emeline Perthame

High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model

Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini

MFE: Towards reproducible meta-feature extraction

Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization

Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh

Bayesian Model Selection with Graph Structured Sparsity

Youngseok Kim, Chao Gao

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He

Change Point Estimation in a Dynamic Stochastic Block Model

Monika Bhattacharjee, Moulinath Banerjee, George Michailidis

Quadratic Decomposable Submodular Function Minimization: Theory and Practice

Pan Li, Niao He, Olgica Milenkovic

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Sparse Projection Oblique Randomer Forests

Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic

Stochastic Nested Variance Reduction for Nonconvex Optimization

Dongruo Zhou, Pan Xu, Quanquan Gu

AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)

Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowe

Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size

Francois Kamper, Sarel J. Steel, Johan A. du Preez

General Latent Feature Models for Heterogeneous Datasets

Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani

Joint Causal Inference from Multiple Contexts

Joris M. Mooij, Sara Magliacane, Tom Claassen

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings

Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi

Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization

Zhao-Rong Lai, Liming Tan, Xiaotian Wu, Liangda Fang

pyDML: A Python Library for Distance Metric Learning

Juan Luis Suárez, Salvador García, Francisco Herrera

Cornac: A Comparative Framework for Multimodal Recommender Systems

Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw

Minimax Nonparametric Parallelism Test

Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong

Distributed Kernel Ridge Regression with Communications

Shao-Bo Lin, Di Wang, Ding-Xuan Zhou

Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients

Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Ming Yu, Varun Gupta, Mladen Kolar

Probabilistic Symmetries and Invariant Neural Networks

Benjamin Bloem-Reddy, Yee Whye Teh

Causal Discovery from Heterogeneous/Nonstationary Data

Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero

Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators

Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi

Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data

Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque

Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure

Yating Liu, Gilles Pagès

Effective Ways to Build and Evaluate Individual Survival Distributions

Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions

Christiane Görgen, Manuele Leonelli

Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis

Xiaoming Yuan, Shangzhi Zeng, Jin Zhang

Sequential change-point detection in high-dimensional Gaussian graphical models

Hossein Keshavarz, George Michaildiis, Yves Atchade

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins

Memoryless Sequences for General Losses

Rafael Frongillo, Andrew Nobel

Quantile Graphical Models: a Bayesian Approach

Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick

Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms

Malte Probst, Franz Rothlauf

Multi-Player Bandits: The Adversarial Case

Pragnya Alatur, Kfir Y. Levy, Andreas Krause

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning

Anis Elgabli, Jihong Park, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal

Identifiability of Additive Noise Models Using Conditional Variances

Gunwoong Park

High-dimensional Gaussian graphical models on network-linked data

Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu

Scalable Approximate MCMC Algorithms for the Horseshoe Prior

James Johndrow, Paulo Orenstein, Anirban Bhattacharya

(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov

Estimation of a Low-rank Topic-Based Model for Information Cascades

Ming Yu, Varun Gupta, Mladen Kolar

Representation Learning for Dynamic Graphs: A Survey

Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi

Union of Low-Rank Tensor Spaces: Clustering and Completion

Morteza Ashraphijuo, Xiaodong Wang

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics

Xi Chen, Simon S. Du, Xin T. Tong

The weight function in the subtree kernel is decisive

Romain Azaïs, Florian Ingels

WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions

Edgar Dobriban, Yue Sheng

Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients

Yu Liu, Kris De Brabanter

Community-Based Group Graphical Lasso

Eugen Pircalabelu, Gerda Claeskens

Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning

Yu Wang, Siqi Wu, Bin Yu

Generalized Optimal Matching Methods for Causal Inference

Nathan Kallus

Multiparameter Persistence Landscapes

Oliver Vipond

Kymatio: Scattering Transforms in Python

Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette

Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis

Salar Fattahi, Somayeh Sojoudi

Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions

Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis

Self-paced Multi-view Co-training

Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes

Peter D. Grünwald, Nishant A. Mehta

Conjugate Gradients for Kernel Machines

Simon Bartels, Philipp Hennig

GraKeL: A Graph Kernel Library in Python

Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis

High-Dimensional Inference for Cluster-Based Graphical Models

Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu