JMLR 2022

353 papers

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences

Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood

A Closer Look at Embedding Propagation for Manifold Smoothing

Diego Velazquez, Pau Rodriguez, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzalez

Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems

Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz

Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials

Daiqi Gao, Yufeng Liu, Donglin Zeng

Projected Robust PCA with Application to Smooth Image Recovery

Long Feng, Junhui Wang

Double Spike Dirichlet Priors for Structured Weighting

Huiming Lin, Meng Li

Quantile regression with ReLU Networks: Estimators and minimax rates

OSCAR HERNAN MADRID PADILLA, Wesley Tansey, Yanzhen Chen

Multivariate Boosted Trees and Applications to Forecasting and Control

Lorenzo Nespoli, Vasco Medici

Mappings for Marginal Probabilities with Applications to Models in Statistical Physics

Mehdi Molkaraie

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables

Yaniv Yacoby, Weiwei Pan, Finale Doshi-velez

Tree-based Node Aggregation in Sparse Graphical Models

Ines Wilms, Jacob Bien

Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure

Yang Ni, Francesco C. Stingo, Veerabhadran Baladandayuthapani

Weakly Supervised Disentangled Generative Causal Representation Learning

Xinwei Shen, Furui Liu, Hanze Dong, Qing LIAN, Zhitang Chen

MALTS: Matching After Learning to Stretch

Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization

Zhize Li, Jian Li

A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates

Prashanth L.A., Sanjay P. Bhat

Nonparametric Principal Subspace Regression

Yang Zhou, Mark Koudstaal, Dengdeng Yu, Dehan Kong, Fang Yao

KoPA: Automated Kronecker Product Approximation

Chencheng Cai, Rong Chen, Han Xiao

Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets

Arnak S. Dalalyan, Avetik Karagulyan, Lionel Riou-Durand

Change point localization in dependent dynamic nonparametric random dot product graphs

OSCAR HERNAN MADRID PADILLA, Yi Yu, Carey E. Priebe

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett

Gaussian Process Boosting

Fabio Sigrist

Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation

Hiroaki Sasaki, Takashi Takenouchi

Multi-Task Dynamical Systems

Alex Bird, Christopher K. I. Williams, Christopher Hawthorne

Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration

Congliang Chen, Li Shen, Fangyu Zou, Wei Liu

Asymptotic Study of Stochastic Adaptive Algorithms in Non-convex Landscape

Sébastien Gadat, Ioana Gavra

Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits

Hao Chen, Lili Zheng, Raed AL Kontar, Garvesh Raskutti

Underspecification Presents Challenges for Credibility in Modern Machine Learning

Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi

Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions

Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh

Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance

Lucas Mentch, Siyu Zhou

On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping

Qiang Zhou, Sinno Jialin Pan

Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization

Huan Li, Zhouchen Lin, Yongchun Fang

Behavior Priors for Efficient Reinforcement Learning

Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks

Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar, Umut Simsekli, Lingjiong Zhu

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-paz, Michele Sebag

Learning Green's functions associated with time-dependent partial differential equations

Nicolas Boulle, Seick Kim, Tianyi Shi, Alex Townsend

Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee

Bo Shen, Weijun Xie, Zhenyu (James) Kong

Kernel Partial Correlation Coefficient --- a Measure of Conditional Dependence

Zhen Huang, NABARUN DEB, Bodhisattva Sen

Learning Operators with Coupled Attention

Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint

Yoav Freund, Yi-An Ma, Tong Zhang

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features

Lars H. B. Olsen, Ingrid K. Glad, Martin Jullum, Kjersti Aas

Multi-Agent Multi-Armed Bandits with Limited Communication

Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli

Efficient Inference for Dynamic Flexible Interactions of Neural Populations

Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, YIXUAN ZHANG

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review

Michael Pearce, Elena A. Erosheva

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs

Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan

tntorch: Tensor Network Learning with PyTorch

Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler

Nonconvex Matrix Completion with Linearly Parameterized Factors

Ji Chen, Xiaodong Li, Zongming Ma

Stochastic DCA with Variance Reduction and Applications in Machine Learning

Hoai An Le Thi, Hoang Phuc Hau Luu, Hoai Minh Le, Tao Pham Dinh

Contraction rates for sparse variational approximations in Gaussian process regression

Dennis Nieman, Botond Szabo, Harry van Zanten

Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable

Baoluo Sun, Yifan Cui, Eric Tchetgen Tchetgen

Testing Whether a Learning Procedure is Calibrated

Jon Cockayne, Matthew M. Graham, Chris J. Oates, T. J. Sullivan, Onur Teymur

abess: A Fast Best-Subset Selection Library in Python and R

Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang

Three rates of convergence or separation via U-statistics in a dependent framework

Quentin Duchemin, Yohann De Castro, Claire Lacour

A Nonconvex Framework for Structured Dynamic Covariance Recovery

Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo

A Forward Approach for Sufficient Dimension Reduction in Binary Classification

Jongkyeong Kang, Seung Jun Shin

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions

Subha Maity, Yuekai Sun, Moulinath Banerjee

InterpretDL: Explaining Deep Models in PaddlePaddle

Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen

Universal Approximation Theorems for Differentiable Geometric Deep Learning

Anastasis Kratsios, Léonie Papon

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

Yang Yu, Shih-Kang Chao, Guang Cheng

Uniform deconvolution for Poisson Point Processes

Anna Bonnet, Claire Lacour, Franck Picard, Vincent Rivoirard

Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression

Wenjia Wang, Bing-Yi Jing

A Bregman Learning Framework for Sparse Neural Networks

Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Bürger

Deep Limits and a Cut-Off Phenomenon for Neural Networks

Benny Avelin, Anders Karlsson

Clustering with Semidefinite Programming and Fixed Point Iteration

Pedro Felzenszwalb, Caroline Klivans, Alice Paul

Learning to Optimize: A Primer and A Benchmark

Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu

Active Structure Learning of Bayesian Networks in an Observational Setting

Noa Ben-David, Sivan Sabato

Adversarial Classification: Necessary Conditions and Geometric Flows

Nicolás García Trillos, Ryan Murray

Estimating Density Models with Truncation Boundaries using Score Matching

Song Liu, Takafumi Kanamori, Daniel J. Williams

A Primer for Neural Arithmetic Logic Modules

Bhumika Mistry, Katayoun Farrahi, Jonathon Hare

Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models

Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati

Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution

Yichen Zhou, Giles Hooker

Extensions to the Proximal Distance Method of Constrained Optimization

Alfonso Landeros, OSCAR HERNAN MADRID PADILLA, Hua Zhou, Kenneth Lange

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

David Holzmüller, Ingo Steinwart

Matrix Completion with Covariate Information and Informative Missingness

Huaqing Jin, Yanyuan Ma, Fei Jiang

KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints

Aurélien Garivier, Hedi Hadiji, Pierre Menard, Gilles Stoltz

Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon

Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms

Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma

Signature Moments to Characterize Laws of Stochastic Processes

Ilya Chevyrev, Harald Oberhauser

Improved Generalization Bounds for Adversarially Robust Learning

Idan Attias, Aryeh Kontorovich, Yishay Mansour

Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models

Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki

Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training

Diego Granziol, Stefan Zohren, Stephen Roberts

Projection-free Distributed Online Learning with Sublinear Communication Complexity

Yuanyu Wan, Guanghui Wang, Wei-Wei Tu, Lijun Zhang

Interlocking Backpropagation: Improving depthwise model-parallelism

Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst

Scalable and Efficient Hypothesis Testing with Random Forests

Tim Coleman, Wei Peng, Lucas Mentch

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

Hai Shu, Zhe Qu, Hongtu Zhu

A Worst Case Analysis of Calibrated Label Ranking Multi-label Classification Method

Lucas Henrique Sousa Mello, Flávio Miguel Varejão, Alexandre Loureiros Rodrigues

Unbiased estimators for random design regression

Michał Dereziński, Manfred K. Warmuth, Daniel Hsu

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects

Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

Improved Classification Rates for Localized SVMs

Ingrid Blaschzyk, Ingo Steinwart

Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation

Antoine Dedieu, Rahul Mazumder, Haoyue Wang

Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements

Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods

Dachao Lin, Haishan Ye, Zhihua Zhang

Topologically penalized regression on manifolds

Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik

Fairness-Aware PAC Learning from Corrupted Data

Nikola Konstantinov, Christoph H. Lampert

Structure Learning for Directed Trees

Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters

ktrain: A Low-Code Library for Augmented Machine Learning

Arun S. Maiya

A universally consistent learning rule with a universally monotone error

Vladimir Pestov

Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification

Yingying Zhang, Yan-Yong Zhao, Heng Lian

Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks

Guy Hacohen, Daphna Weinshall

Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach

Yanwei Jia, Xun Yu Zhou