JMLR 2020

252 papers

Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra

Vardan Papyan

Online matrix factorization for Markovian data and applications to Network Dictionary Learning

Hanbaek Lyu, Deanna Needell, Laura Balzano

High-dimensional quantile tensor regression

Wenqi Lu, Zhongyi Zhu, Heng Lian

Learning Mixed Latent Tree Models

Can Zhou, Xiaofei Wang, Jianhua Guo

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky

Adaptive Rates for Total Variation Image Denoising

Francesco Ortelli, Sara van de Geer

On Efficient Adjustment in Causal Graphs

Janine Witte, Leonard Henckel, Marloes H. Maathuis, Vanessa Didelez

A Group-Theoretic Framework for Data Augmentation

Shuxiao Chen, Edgar Dobriban, Jane H. Lee

Rank-based Lasso - efficient methods for high-dimensional robust model selection

Wojciech Rejchel, Małgorzata Bogdan

Best Practices for Scientific Research on Neural Architecture Search

Marius Lindauer, Frank Hutter

Fair Data Adaptation with Quantile Preservation

Drago Plecko, Nicolai Meinshausen

Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables

Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya

Risk Bounds for Reservoir Computing

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection

Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen, César L. C. Mattos, Amauri H. Souza Júnior

algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD

Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui

The Error-Feedback framework: SGD with Delayed Gradients

Sebastian U. Stich, Sai Praneeth Karimireddy

Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information

Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur

Convex Programming for Estimation in Nonlinear Recurrent Models

Sohail Bahmani, Justin Romberg

Dual Extrapolation for Sparse GLMs

Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

Robust high dimensional learning for Lipschitz and convex losses

Chinot Geoffrey, Lecué Guillaume, Lerasle Matthieu

Spectral Deconfounding via Perturbed Sparse Linear Models

Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen

Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion

Dimitris Bertsimas, Michael Lingzhi Li

Stable Regression: On the Power of Optimization over Randomization

Dimitris Bertsimas, Ivan Paskov

Nonparametric graphical model for counts

Arkaprava Roy, David B Dunson

Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications

Frederik Heber, Žofia Trst’anová, Benedict Leimkuhler

Significance Tests for Neural Networks

Enguerrand Horel, Kay Giesecke

A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems

Peipei Tang, Chengjing Wang, Defeng Sun, Kim-Chuan Toh

Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering

Tao Jiang, Stephen Vavasis, Chen Wen Zhai

Ultra-High Dimensional Single-Index Quantile Regression

Yuankun Zhang, Heng Lian, Yan Yu

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou

Theory of Curriculum Learning, with Convex Loss Functions

Daphna Weinshall, Dan Amir

Learning Sums of Independent Random Variables with Sparse Collective Support

Anindya De, Philip M. Long, Rocco A. Servedio

Diffeomorphic Learning

Laurent Younes

AdaGrad stepsizes: Sharp convergence over nonconvex landscapes

Rachel Ward, Xiaoxia Wu, Léon Bottou

Spectral bandits

Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko

On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach

Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo

Dynamic Assortment Optimization with Changing Contextual Information

Xi Chen, Yining Wang, Yuan Zhou

Mining Topological Structure in Graphs through Forest Representations

Robin Vandaele, Yvan Saeys, Tijl De Bie

Provable Convex Co-clustering of Tensors

Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang

Multiclass Anomaly Detector: the CS++ Support Vector Machine

Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami

scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn

Sebastian Pölsterl

Random Smoothing Might be Unable to Certify ℓ_∞ Robustness for High-Dimensional Images

Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang

ProtoAttend: Attention-Based Prototypical Learning

Sercan Ö. Arik, Tomas Pfister

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly

Learning Data-adaptive Non-parametric Kernels

Fanghui Liu, Xiaolin Huang, Chen Gong, jie Yang, Li Li

Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging

Baihua He, Yanyan Liu, Yuanshan Wu, Guosheng Yin, Xingqiu Zhao

On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond

Xiao-Tong Yuan, Ping Li

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms

Simon Fischer, Ingo Steinwart

Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data

Martin Slawski, Emanuel Ben-David, Ping Li

Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance

Dalit Engelhardt

A Numerical Measure of the Instability of Mapper-Type Algorithms

Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models

Reza Mohammadi, Matthew Pratola, Maurits Kaptein

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy

Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu

Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms

Yazhen Wang, Shang Wu

Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach

Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou

A determinantal point process for column subset selection

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning

Lucas Lehnert, Michael L. Littman

Conic Optimization for Quadratic Regression Under Sparse Noise

Igor Molybog, Ramtin Madani, Javad Lavaei

Contextual Explanation Networks

Maruan Al-Shedivat, Avinava Dubey, Eric Xing

Learning and Interpreting Multi-Multi-Instance Learning Networks

Alessandro Tibo, Manfred Jaeger, Paolo Frasconi

Semi-parametric Learning of Structured Temporal Point Processes

Ganggang Xu, Ming Wang, Jiangze Bian, Hui Huang, Timothy R. Burch

Adaptive Smoothing for Path Integral Control

Dominik Thalmeier, Hilbert J. Kappen, Simone Totaro, Vicenç Gómez

A Unified q-Memorization Framework for Asynchronous Stochastic Optimization

Bin Gu, Wenhan Xian, Zhouyuan Huo, Cheng Deng, Heng Huang

Beyond Trees: Classification with Sparse Pairwise Dependencies

Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman

Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models

Andrea Rotnitzky, Ezequiel Smucler

Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs

Rui Tuo, Wenjia Wang

Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods

Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M Stuart

Scikit-network: Graph Analysis in Python

Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier

Topology of Deep Neural Networks

Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim

Near-optimal Individualized Treatment Recommendations

Haomiao Meng, Ying-Qi Zhao, Haoda Fu, Xingye Qiao

Distributed High-dimensional Regression Under a Quantile Loss Function

Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor

Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction

Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi

Variational Inference for Computational Imaging Inverse Problems

Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, Roderick Murray-Smith

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster

Optimal Estimation of Sparse Topic Models

Xin Bing, Florentina Bunea, Marten Wegkamp

Breaking the Curse of Nonregularity with Subagging --- Inference of the Mean Outcome under Optimal Treatment Regimes

Chengchun Shi, Wenbin Lu, Rui Song

Wide Neural Networks with Bottlenecks are Deep Gaussian Processes

Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality

Ryumei Nakada, Masaaki Imaizumi

Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes

Emily C. Hector, Peter X.-K. Song

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

Lucas Mentch, Siyu Zhou

Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

William Hoiles, Vikram Krishnamurthy, Kunal Pattanayak

The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization

Dmitry Kobak, Jonathan Lomond, Benoit Sanchez

Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels

Hyebin Song, Ran Dai, Garvesh Raskutti, Rina Foygel Barber

Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes

Nathan Kallus, Masatoshi Uehara

High Dimensional Forecasting via Interpretable Vector Autoregression

William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson

Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group

Yuexiang Zhai, Zitong Yang, Zhenyu Liao, John Wright, Yi Ma

Cramer-Wold Auto-Encoder

Szymon Knop, Przemysław Spurek, Jacek Tabor, Igor Podolak, Marcin Mazur

Trust-Region Variational Inference with Gaussian Mixture Models

Oleg Arenz, Mingjun Zhong, Gerhard Neumann

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

apricot: Submodular selection for data summarization in Python

Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble

Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective

Chao Gao, Yuan Yao, Weizhi ZHU

Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study

Yan Ru Pei, Haik Manukian, Massimiliano Di Ventra

Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion

Jiaying Gu, Qing Zhou

Streamlined Variational Inference with Higher Level Random Effects

Tui H. Nolan, Marianne Menictas, Matt P. Wand

Asymptotic Consistency of α-Rényi-Approximate Posteriors

Prateek Jaiswal, Vinayak Rao, Harsha Honnappa

Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise

Andrei Kulunchakov, Julien Mairal

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality

Miaoyan Wang, Lexin Li

Spectral Algorithms for Community Detection in Directed Networks

Zhe Wang, Yingbin Liang, Pengsheng Ji