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Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.

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Latest papers

ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning
Haiyun He, Hanshu Yan, Vincent Y. F. Tan, 2022.
[abs][pdf][bib]      [code]

Integral Autoencoder Network for Discretization-Invariant Learning
Yong Zheng Ong, Zuowei Shen, Haizhao Yang, 2022.
[abs][pdf][bib]      [code]

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
Chuyang Ke, Jean Honorio, 2022.
[abs][pdf][bib]

De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Adrien Corenflos, Nicolas Chopin, Simo Särkkä, 2022.
[abs][pdf][bib]      [code]

On the Convergence Rates of Policy Gradient Methods
Lin Xiao, 2022.
[abs][pdf][bib]

Nonparametric adaptive control and prediction: theory and randomized algorithms
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine, 2022.
[abs][pdf][bib]

Generalized Resubstitution for Classification Error Estimation
Parisa Ghane, Ulisses Braga-Neto, 2022.
[abs][pdf][bib]

Convergence Guarantees for the Good-Turing Estimator
Amichai Painsky, 2022.
[abs][pdf][bib]

Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions
Chiwoo Park, 2022.
[abs][pdf][bib]      [code]

Nonstochastic Bandits with Composite Anonymous Feedback
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Claudio Gentile, Yishay Mansour, 2022.
[abs][pdf][bib]

Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
Shijun Zhang, Zuowei Shen, Haizhao Yang, 2022.
[abs][pdf][bib]

Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
Yanwei Jia, Xun Yu Zhou, 2022.
[abs][pdf][bib]      [code]

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, João G.M. Araújo, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

The Weighted Generalised Covariance Measure
Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann, 2022.
[abs][pdf][bib]

Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees
Kean Ming Tan, Heather Battey, Wen-Xin Zhou, 2022.
[abs][pdf][bib]

Fast Stagewise Sparse Factor Regression
Kun Chen, Ruipeng Dong, Wanwan Xu, Zemin Zheng, 2022.
[abs][pdf][bib]

Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling
Keru Wu, Scott Schmidler, Yuansi Chen, 2022.
[abs][pdf][bib]

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
Ruixuan Zhao, Xin He, Junhui Wang, 2022.
[abs][pdf][bib]

A Computationally Efficient Framework for Vector Representation of Persistence Diagrams
Kit C Chan, Umar Islambekov, Alexey Luchinsky, Rebecca Sanders, 2022.
[abs][pdf][bib]

Tianshou: A Highly Modularized Deep Reinforcement Learning Library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Functional Linear Regression with Mixed Predictors
Daren Wang, Zifeng Zhao, Yi Yu, Rebecca Willett, 2022.
[abs][pdf][bib]      [code]

Stochastic subgradient for composite convex optimization with functional constraints
Ion Necoara, Nitesh Kumar Singh, 2022.
[abs][pdf][bib]

A Random Matrix Perspective on Random Tensors
José Henrique de M. Goulart, Romain Couillet, Pierre Comon, 2022.
[abs][pdf][bib]

The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett, 2022.
[abs][pdf][bib]

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score
Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen A Smith, Ying-Qi Zhao, 2022.
[abs][pdf][bib]      [code]

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter, 2022.
[abs][pdf][bib]      [code]

A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions
Arnulf Jentzen, Adrian Riekert, 2022.
[abs][pdf][bib]

Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models
Shiwei Lan, 2022.
[abs][pdf][bib]      [code]

Tree-Based Models for Correlated Data
Assaf Rabinowicz, Saharon Rosset, 2022.
[abs][pdf][bib]

Sparse Continuous Distributions and Fenchel-Young Losses
André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae, 2022.
[abs][pdf][bib]      [code]

On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems
Michael Muehlebach, Michael I. Jordan, 2022.
[abs][pdf][bib]      [code]

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States
Shi Dong, Benjamin Van Roy, Zhengyuan Zhou, 2022.
[abs][pdf][bib]

Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation
Minh-Lien Jeanne Nguyen, Claire Lacour, Vincent Rivoirard, 2022.
[abs][pdf][bib]

Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White, 2022.
[abs][pdf][bib]

A Closer Look at Embedding Propagation for Manifold Smoothing
Diego Velazquez, Pau Rodriguez, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzalez, 2022.
[abs][pdf][bib]

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, Richard E. Stearns, 2022.
[abs][pdf][bib]

Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials
Daiqi Gao, Yufeng Liu, Donglin Zeng, 2022.
[abs][pdf][bib]

Projected Robust PCA with Application to Smooth Image Recovery
Long Feng, Junhui Wang, 2022.
[abs][pdf][bib]

Double Spike Dirichlet Priors for Structured Weighting
Huiming Lin, Meng Li, 2022.
[abs][pdf][bib]      [code]

Quantile regression with ReLU Networks: Estimators and minimax rates
Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen, 2022.
[abs][pdf][bib]      [code]

Multivariate Boosted Trees and Applications to Forecasting and Control
Lorenzo Nespoli, Vasco Medici, 2022.
[abs][pdf][bib]      [code]

Mappings for Marginal Probabilities with Applications to Models in Statistical Physics
Mehdi Molkaraie, 2022.
[abs][pdf][bib]

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez, 2022.
[abs][pdf][bib]

Tree-based Node Aggregation in Sparse Graphical Models
Ines Wilms, Jacob Bien, 2022.
[abs][pdf][bib]      [code]

Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure
Yang Ni, Francesco C. Stingo, Veerabhadran Baladandayuthapani, 2022.
[abs][pdf][bib]

Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang, 2022.
[abs][pdf][bib]      [code]

MALTS: Matching After Learning to Stretch
Harsh Parikh, Cynthia Rudin, Alexander Volfovsky, 2022.
[abs][pdf][bib]      [code]

Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization
Zhize Li, Jian Li, 2022.
[abs][pdf][bib]

A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates
Prashanth L.A., Sanjay P. Bhat, 2022.
[abs][pdf][bib]

Nonparametric Principal Subspace Regression
Yang Zhou, Mark Koudstaal, Dengdeng Yu, Dehan Kong, Fang Yao, 2022.
[abs][pdf][bib]

KoPA: Automated Kronecker Product Approximation
Chencheng Cai, Rong Chen, Han Xiao, 2022.
[abs][pdf][bib]

Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets
Arnak S. Dalalyan, Avetik Karagulyan, Lionel Riou-Durand, 2022.
[abs][pdf][bib]

Change point localization in dependent dynamic nonparametric random dot product graphs
Oscar Hernan Madrid Padilla, Yi Yu, Carey E. Priebe, 2022.
[abs][pdf][bib]

An Efficient Sampling Algorithm for Non-smooth Composite Potentials
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett, 2022.
[abs][pdf][bib]

Gaussian Process Boosting
Fabio Sigrist, 2022.
[abs][pdf][bib]      [code]

Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation
Hiroaki Sasaki, Takashi Takenouchi, 2022.
[abs][pdf][bib]

Multi-Task Dynamical Systems
Alex Bird, Christopher K. I. Williams, Christopher Hawthorne, 2022.
[abs][pdf][bib]

Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Congliang Chen, Li Shen, Fangyu Zou, Wei Liu, 2022.
[abs][pdf][bib]

Asymptotic Study of Stochastic Adaptive Algorithms in Non-convex Landscape
Sébastien Gadat, Ioana Gavra, 2022.
[abs][pdf][bib]

Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti, 2022.
[abs][pdf][bib]

Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley, 2022.
[abs][pdf][bib]

Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh, 2022.
[abs][pdf][bib]

Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance
Lucas Mentch, Siyu Zhou, 2022.
[abs][pdf][bib]

On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping
Qiang Zhou, Sinno Jialin Pan, 2022.
[abs][pdf][bib]

Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization
Huan Li, Zhouchen Lin, Yongchun Fang, 2022.
[abs][pdf][bib]

Behavior Priors for Efficient Reinforcement Learning
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess, 2022.
[abs][pdf][bib]

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Alireza Fallah, Mert Gürbüzbalaban, Asuman Ozdaglar, Umut Şimşekli, Lingjiong Zhu, 2022.
[abs][pdf][bib]

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag, 2022.
[abs][pdf][bib]      [code]

Learning Green's functions associated with time-dependent partial differential equations
Nicolas Boullé, Seick Kim, Tianyi Shi, Alex Townsend, 2022.
[abs][pdf][bib]

Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
Bo Shen, Weijun Xie, Zhenyu (James) Kong, 2022.
[abs][pdf][bib]      [code]

Kernel Partial Correlation Coefficient --- a Measure of Conditional Dependence
Zhen Huang, Nabarun Deb, Bodhisattva Sen, 2022.
[abs][pdf][bib]

Learning Operators with Coupled Attention
Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris, 2022.
[abs][pdf][bib]

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
Yoav Freund, Yi-An Ma, Tong Zhang, 2022.
[abs][pdf][bib]

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, 2022.
[abs][pdf][bib]      [code]

Multi-Agent Multi-Armed Bandits with Limited Communication
Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli, 2022.
[abs][pdf][bib]

Efficient Inference for Dynamic Flexible Interactions of Neural Populations
Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu, 2022.
[abs][pdf][bib]

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Michael Pearce, Elena A. Erosheva, 2022.
[abs][pdf][bib]

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long, 2022.
[abs][pdf][bib]      [code]

tntorch: Tensor Network Learning with PyTorch
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler, 2022.
[abs][pdf][bib]      [code]

Nonconvex Matrix Completion with Linearly Parameterized Factors
Ji Chen, Xiaodong Li, Zongming Ma, 2022.
[abs][pdf][bib]

Stochastic DCA with Variance Reduction and Applications in Machine Learning
Hoai An Le Thi, Hoang Phuc Hau Luu, Hoai Minh Le, Tao Pham Dinh, 2022.
[abs][pdf][bib]

Contraction rates for sparse variational approximations in Gaussian process regression
Dennis Nieman, Botond Szabo, Harry van Zanten, 2022.
[abs][pdf][bib]

Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
Baoluo Sun, Yifan Cui, Eric Tchetgen Tchetgen, 2022.
[abs][pdf][bib]

Testing Whether a Learning Procedure is Calibrated
Jon Cockayne, Matthew M. Graham, Chris J. Oates, T. J. Sullivan, Onur Teymur, 2022.
[abs][pdf][bib]

abess: A Fast Best-Subset Selection Library in Python and R
Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Three rates of convergence or separation via U-statistics in a dependent framework
Quentin Duchemin, Yohann De Castro, Claire Lacour, 2022.
[abs][pdf][bib]      [code]

A Nonconvex Framework for Structured Dynamic Covariance Recovery
Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo, 2022.
[abs][pdf][bib]      [code]

A Forward Approach for Sufficient Dimension Reduction in Binary Classification
Jongkyeong Kang, Seung Jun Shin, 2022.
[abs][pdf][bib]

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
Subha Maity, Yuekai Sun, Moulinath Banerjee, 2022.
[abs][pdf][bib]      [code]

InterpretDL: Explaining Deep Models in PaddlePaddle
Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, Dejing Dou, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Universal Approximation Theorems for Differentiable Geometric Deep Learning
Anastasis Kratsios, Léonie Papon, 2022.
[abs][pdf][bib]

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
Yang Yu, Shih-Kang Chao, Guang Cheng, 2022.
[abs][pdf][bib]      [code]

Uniform deconvolution for Poisson Point Processes
Anna Bonnet, Claire Lacour, Franck Picard, Vincent Rivoirard, 2022.
[abs][pdf][bib]

Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Wenjia Wang, Bing-Yi Jing, 2022.
[abs][pdf][bib]

A Bregman Learning Framework for Sparse Neural Networks
Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger, 2022.
[abs][pdf][bib]      [code]

Deep Limits and a Cut-Off Phenomenon for Neural Networks
Benny Avelin, Anders Karlsson, 2022.
[abs][pdf][bib]

Clustering with Semidefinite Programming and Fixed Point Iteration
Pedro Felzenszwalb, Caroline Klivans, Alice Paul, 2022.
[abs][pdf][bib]

Learning to Optimize: A Primer and A Benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin, 2022.
[abs][pdf][bib]      [code]

Active Structure Learning of Bayesian Networks in an Observational Setting
Noa Ben-David, Sivan Sabato, 2022.
[abs][pdf][bib]      [code]

Adversarial Classification: Necessary Conditions and Geometric Flows
Nicolás García Trillos, Ryan Murray, 2022.
[abs][pdf][bib]

Estimating Density Models with Truncation Boundaries using Score Matching
Song Liu, Takafumi Kanamori, Daniel J. Williams, 2022.
[abs][pdf][bib]      [code]

A Primer for Neural Arithmetic Logic Modules
Bhumika Mistry, Katayoun Farrahi, Jonathon Hare, 2022.
[abs][pdf][bib]      [code]

Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models
Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati, 2022.
[abs][pdf][bib]

Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
Yichen Zhou, Giles Hooker, 2022.
[abs][pdf][bib]      [code]

Extensions to the Proximal Distance Method of Constrained Optimization
Alfonso Landeros, Oscar Hernan Madrid Padilla, Hua Zhou, Kenneth Lange, 2022.
[abs][pdf][bib]      [code]

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
David Holzmüller, Ingo Steinwart, 2022.
[abs][pdf][bib]      [code]

Matrix Completion with Covariate Information and Informative Missingness
Huaqing Jin, Yanyuan Ma, Fei Jiang, 2022.
[abs][pdf][bib]      [code]

KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints
Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz, 2022.
[abs][pdf][bib]

Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon
Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang, 2022.
[abs][pdf][bib]

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms
Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney, 2022.
[abs][pdf][bib]

Signature Moments to Characterize Laws of Stochastic Processes
Ilya Chevyrev, Harald Oberhauser, 2022.
[abs][pdf][bib]

Improved Generalization Bounds for Adversarially Robust Learning
Idan Attias, Aryeh Kontorovich, Yishay Mansour, 2022.
[abs][pdf][bib]

Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman, 2022.
[abs][pdf][bib]

Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
Diego Granziol, Stefan Zohren, Stephen Roberts, 2022.
[abs][pdf][bib]

Projection-free Distributed Online Learning with Sublinear Communication Complexity
Yuanyu Wan, Guanghui Wang, Wei-Wei Tu, Lijun Zhang, 2022.
[abs][pdf][bib]

Interlocking Backpropagation: Improving depthwise model-parallelism
Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal, 2022.
[abs][pdf][bib]

Scalable and Efficient Hypothesis Testing with Random Forests
Tim Coleman, Wei Peng, Lucas Mentch, 2022.
[abs][pdf][bib]

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
Hai Shu, Zhe Qu, Hongtu Zhu, 2022.
[abs][pdf][bib]      [code]

A Worst Case Analysis of Calibrated Label Ranking Multi-label Classification Method
Lucas Henrique Sousa Mello, Flávio Miguel Varejão, Alexandre Loureiros Rodrigues, 2022.
[abs][pdf][bib]

Unbiased estimators for random design regression
Michał Dereziński, Manfred K. Warmuth, Daniel Hsu, 2022.
[abs][pdf][bib]

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag, 2022.
[abs][pdf][bib]

Improved Classification Rates for Localized SVMs
Ingrid Blaschzyk, Ingo Steinwart, 2022.
[abs][pdf][bib]

Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation
Antoine Dedieu, Rahul Mazumder, Haoyue Wang, 2022.
[abs][pdf][bib]

Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi, 2022.
[abs][pdf][bib]      [code]

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods
Dachao Lin, Haishan Ye, Zhihua Zhang, 2022.
[abs][pdf][bib]

Topologically penalized regression on manifolds
Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard, Clément Levrard, Wolfgang Polonik, 2022.
[abs][pdf][bib]      [code]

Fairness-Aware PAC Learning from Corrupted Data
Nikola Konstantinov, Christoph H. Lampert, 2022.
[abs][pdf][bib]

Structure Learning for Directed Trees
Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters, 2022.
[abs][pdf][bib]      [code]

ktrain: A Low-Code Library for Augmented Machine Learning
Arun S. Maiya, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

A universally consistent learning rule with a universally monotone error
Vladimir Pestov, 2022.
[abs][pdf][bib]

Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification
Yingying Zhang, Yan-Yong Zhao, Heng Lian, 2022.
[abs][pdf][bib]

Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks
Guy Hacohen, Daphna Weinshall, 2022.
[abs][pdf][bib]

Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
Yanwei Jia, Xun Yu Zhou, 2022.
[abs][pdf][bib]      [code]

Truncated Emphatic Temporal Difference Methods for Prediction and Control
Shangtong Zhang, Shimon Whiteson, 2022.
[abs][pdf][bib]      [code]

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-Yves Oudeyer, 2022.
[abs][pdf][bib]

Universal Approximation of Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner, 2022.
[abs][pdf][bib]

EV-GAN: Simulation of extreme events with ReLU neural networks
Michaël Allouche, Stéphane Girard, Emmanuel Gobet, 2022.
[abs][pdf][bib]

Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon, 2022.
[abs][pdf][bib]      [code]

Online Nonnegative CP-dictionary Learning for Markovian Data
Hanbaek Lyu, Christopher Strohmeier, Deanna Needell, 2022.
[abs][pdf][bib]      [code]

On the Robustness to Misspecification of α-posteriors and Their Variational Approximations
Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush, Amilcar Velez, 2022.
[abs][pdf][bib]

Adversarial Robustness Guarantees for Gaussian Processes
Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska, 2022.
[abs][pdf][bib]      [code]

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
Andrew Patterson, Adam White, Martha White, 2022.
[abs][pdf][bib]      [code]

A Momentumized, Adaptive, Dual Averaged Gradient Method
Aaron Defazio, Samy Jelassi, 2022.
[abs][pdf][bib]      [code]

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning
Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman, 2022.
[abs][pdf][bib]

A Perturbation-Based Kernel Approximation Framework
Roy Mitz, Yoel Shkolnisky, 2022.
[abs][pdf][bib]      [code]

The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Konstantinos Pantazis, Avanti Athreya, Jesus Arroyo, William N Frost, Evan S Hill, Vince Lyzinski, 2022.
[abs][pdf][bib]

Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
Yahya Sattar, Samet Oymak, 2022.
[abs][pdf][bib]

No Weighted-Regret Learning in Adversarial Bandits with Delays
Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet, 2022.
[abs][pdf][bib]

Exact simulation of diffusion first exit times: algorithm acceleration
Samuel Herrmann, Cristina Zucca, 2022.
[abs][pdf][bib]      [code]

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport
Tianyi Lin, Nhat Ho, Michael I. Jordan, 2022.
[abs][pdf][bib]

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok, 2022.
[abs][pdf][bib]      [code]

Recovery and Generalization in Over-Realized Dictionary Learning
Jeremias Sulam, Chong You, Zhihui Zhu, 2022.
[abs][pdf][bib]

Transfer Learning in Information Criteria-based Feature Selection
Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao, 2022.
[abs][pdf][bib]      [code]

Manifold Coordinates with Physical Meaning
Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen, 2022.
[abs][pdf][bib]      [code]

An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas, 2022.
[abs][pdf][bib]      [code]

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
Julie Nutini, Issam Laradji, Mark Schmidt, 2022.
[abs][pdf][bib]      [code]

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli, 2022.
[abs][pdf][bib]

On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri, 2022.
[abs][pdf][bib]

Power Iteration for Tensor PCA
Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng, 2022.
[abs][pdf][bib]

Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Haoyuan Chen, Liang Ding, Rui Tuo, 2022.
[abs][pdf][bib]

Neural Estimation of Statistical Divergences
Sreejith Sreekumar, Ziv Goldfeld, 2022.
[abs][pdf][bib]

Foolish Crowds Support Benign Overfitting
Niladri S. Chatterji, Philip M. Long, 2022.
[abs][pdf][bib]

Darts: User-Friendly Modern Machine Learning for Time Series
Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
Jian-Feng Cai, Jingyang Li, Dong Xia, 2022.
[abs][pdf][bib]

Depth separation beyond radial functions
Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna, 2022.
[abs][pdf][bib]

Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case
Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander, 2022.
[abs][pdf][bib]

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
William Fedus, Barret Zoph, Noam Shazeer, 2022.
[abs][pdf][bib]      [code]

A spectral-based analysis of the separation between two-layer neural networks and linear methods
Lei Wu, Jihao Long, 2022.
[abs][pdf][bib]

Under-bagging Nearest Neighbors for Imbalanced Classification
Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin, 2022.
[abs][pdf][bib]

OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems
Chelsea Sidrane, Amir Maleki, Ahmed Irfan, Mykel J. Kochenderfer, 2022.
[abs][pdf][bib]      [code]

An Error Analysis of Generative Adversarial Networks for Learning Distributions
Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, Yunfei Yang, 2022.
[abs][pdf][bib]

Cauchy–Schwarz Regularized Autoencoder
Linh Tran, Maja Pantic, Marc Peter Deisenroth, 2022.
[abs][pdf][bib]

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma, 2022.
[abs][pdf][bib]      [code]

The Two-Sided Game of Googol
José Correa, Andrés Cristi, Boris Epstein, José Soto, 2022.
[abs][pdf][bib]

Sum of Ranked Range Loss for Supervised Learning
Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu, 2022.
[abs][pdf][bib]      [code]

Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces
Masaaki Imaizumi, Kenji Fukumizu, 2022.
[abs][pdf][bib]

EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation
Jun Ho Yoon, Seyoung Kim, 2022.
[abs][pdf][bib]      [code]

Conditions and Assumptions for Constraint-based Causal Structure Learning
Kayvan Sadeghi, Terry Soo, 2022.
[abs][pdf][bib]

Bayesian subset selection and variable importance for interpretable prediction and classification
Daniel R. Kowal, 2022.
[abs][pdf][bib]      [code]

IALE: Imitating Active Learner Ensembles
Christoffer Löffler, Christopher Mutschler, 2022.
[abs][pdf][bib]      [code]

Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
Bokun Wang, Shiqian Ma, Lingzhou Xue, 2022.
[abs][pdf][bib]

Globally Injective ReLU Networks
Michael Puthawala, Konik Kothari, Matti Lassas, Ivan Dokmanić, Maarten de Hoop, 2022.
[abs][pdf][bib]

Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency
F. Richard Guo, Emilija Perković, 2022.
[abs][pdf][bib]      [code]

The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures
Nir Weinberger, Guy Bresler, 2022.
[abs][pdf][bib]

Sufficient reductions in regression with mixed predictors
Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia, Pamela Llop, Diego Tomassi, 2022.
[abs][pdf][bib]      [code]

Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis
Xiangyu Yang, Jiashan Wang, Hao Wang, 2022.
[abs][pdf][bib]

Total Stability of SVMs and Localized SVMs
Hannes Köhler, Andreas Christmann, 2022.
[abs][pdf][bib]

Distributed Learning of Finite Gaussian Mixtures
Qiong Zhang, Jiahua Chen, 2022.
[abs][pdf][bib]      [code]

PECOS: Prediction for Enormous and Correlated Output Spaces
Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon, 2022.
[abs][pdf][bib]      [code]

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
Daniel Sanz-Alonso, Ruiyi Yang, 2022.
[abs][pdf][bib]

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
Chunxiao Li, Cynthia Rudin, Tyler H. McCormick, 2022.
[abs][pdf][bib]

Attraction-Repulsion Spectrum in Neighbor Embeddings
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak, 2022.
[abs][pdf][bib]      [code]

Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach
Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat, 2022.
[abs][pdf][bib]

Regularized K-means Through Hard-Thresholding
Jakob Raymaekers, Ruben H. Zamar, 2022.
[abs][pdf][bib]      [code]

Gauss-Legendre Features for Gaussian Process Regression
Paz Fink Shustin, Haim Avron, 2022.
[abs][pdf][bib]

When Hardness of Approximation Meets Hardness of Learning
Eran Malach, Shai Shalev-Shwartz, 2022.
[abs][pdf][bib]

Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling
Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang, 2022.
[abs][pdf][bib]

Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy, 2022.
[abs][pdf][bib]

Generalized Ambiguity Decomposition for Ranking Ensemble Learning
Hongzhi Liu, Yingpeng Du, Zhonghai Wu, 2022.
[abs][pdf][bib]

CD-split and HPD-split: Efficient Conformal Regions in High Dimensions
Rafael Izbicki, Gilson Shimizu, Rafael B. Stern, 2022.
[abs][pdf][bib]      [code]

Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing
Chao Shen, Yu-Ting Lin, Hau-Tieng Wu, 2022.
[abs][pdf][bib]

A Distribution Free Conditional Independence Test with Applications to Causal Discovery
Zhanrui Cai, Runze Li, Yaowu Zhang, 2022.
[abs][pdf][bib]

Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior
Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky, Sanvesh Srivastava, 2022.
[abs][pdf][bib]

Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
Yichi Zhang, Molei Liu, Matey Neykov, Tianxi Cai, 2022.
[abs][pdf][bib]      [code]

FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
Boxin Zhao, Y. Samuel Wang, Mladen Kolar, 2022.
[abs][pdf][bib]      [code]

Dependent randomized rounding for clustering and partition systems with knapsack constraints
David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh, 2022.
[abs][pdf][bib]

Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures
Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster, 2022.
[abs][pdf][bib]

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Yuling Yao, Aki Vehtari, Andrew Gelman, 2022.
[abs][pdf][bib]

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos, 2022.
[abs][pdf][bib]

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec, 2022.
[abs][pdf][bib]      [code]

Joint Inference of Multiple Graphs from Matrix Polynomials
Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra, 2022.
[abs][pdf][bib]

Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling
Gábor Melis, András György, Phil Blunsom, 2022.
[abs][pdf][bib]

All You Need is a Good Functional Prior for Bayesian Deep Learning
Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone, 2022.
[abs][pdf][bib]      [code]

A Kernel Two-Sample Test for Functional Data
George Wynne, Andrew B. Duncan, 2022.
[abs][pdf][bib]

Batch Normalization Preconditioning for Neural Network Training
Susanna Lange, Kyle Helfrich, Qiang Ye, 2022.
[abs][pdf][bib]

Multiple-Splitting Projection Test for High-Dimensional Mean Vectors
Wanjun Liu, Xiufan Yu, Runze Li, 2022.
[abs][pdf][bib]

Generalized Sparse Additive Models
Asad Haris, Noah Simon, Ali Shojaie, 2022.
[abs][pdf][bib]      [code]

Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method
Alex Olshevsky, 2022.
[abs][pdf][bib]      [code]

Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng, 2022.
[abs][pdf][bib]

Are All Layers Created Equal?
Chiyuan Zhang, Samy Bengio, Yoram Singer, 2022.
[abs][pdf][bib]

New Insights for the Multivariate Square-Root Lasso
Aaron J. Molstad, 2022.
[abs][pdf][bib]      [code]

On the Complexity of Approximating Multimarginal Optimal Transport
Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan, 2022.
[abs][pdf][bib]

Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity
Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra, 2022.
[abs][pdf][bib]

Additive nonlinear quantile regression in ultra-high dimension
Ben Sherwood, Adam Maidman, 2022.
[abs][pdf][bib]

The AIM and EM Algorithms for Learning from Coarse Data
Manfred Jaeger, 2022.
[abs][pdf][bib]      [code]

Sparse Additive Gaussian Process Regression
Hengrui Luo, Giovanni Nattino, Matthew T. Pratola, 2022.
[abs][pdf][bib]

A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators
Xun Zhang, William B. Haskell, Zhisheng Ye, 2022.
[abs][pdf][bib]

Causal Classification: Treatment Effect Estimation vs. Outcome Prediction
Carlos Fernández-Loría, Foster Provost, 2022.
[abs][pdf][bib]

A Statistical Approach for Optimal Topic Model Identification
Craig M. Lewis, Francesco Grossetti, 2022.
[abs][pdf][bib]

Inherent Tradeoffs in Learning Fair Representations
Han Zhao, Geoffrey J. Gordon, 2022.
[abs][pdf][bib]

solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
Terrance D. Savitsky, Matthew R.Williams, Jingchen Hu, 2022.
[abs][pdf][bib]

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python
Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

LinCDE: Conditional Density Estimation via Lindsey's Method
Zijun Gao, Trevor Hastie, 2022.
[abs][pdf][bib]

Toolbox for Multimodal Learn (scikit-multimodallearn)
Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette, 2022. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
Luong-Ha Nguyen, James-A. Goulet, 2022.
[abs][pdf][bib]

Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection
Xinyi Wang, Lang Tong, 2022.
[abs][pdf][bib]

Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis
Zhiyan Ding, Shi Chen, Qin Li, Stephen J. Wright, 2022.
[abs][pdf][bib]

Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans, 2022.
[abs][pdf][bib]      [code]

Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent
Wanrong Zhu, Zhipeng Lou, Wei Biao Wu, 2022.
[abs][pdf][bib]

Optimal Transport for Stationary Markov Chains via Policy Iteration
Kevin O'Connor, Kevin McGoff, Andrew B. Nobel, 2022.
[abs][pdf][bib]      [code]

PAC Guarantees and Effective Algorithms for Detecting Novel Categories
Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich, 2022.
[abs][pdf][bib]      [code]

Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes, 2022.
[abs][pdf][bib]

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li, 2022.
[abs][pdf][bib]

The correlation-assisted missing data estimator
Timothy I. Cannings, Yingying Fan, 2022.
[abs][pdf][bib]

Structure-adaptive Manifold Estimation
Nikita Puchkin, Vladimir Spokoiny, 2022.
[abs][pdf][bib]

(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics
Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet, 2022.
[abs][pdf][bib]

Score Matched Neural Exponential Families for Likelihood-Free Inference
Lorenzo Pacchiardi, Ritabrata Dutta, 2022.
[abs][pdf][bib]      [code]

Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Matteo Pegoraro, Mario Beraha, 2022.
[abs][pdf][bib]      [code]

Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang, 2022.
[abs][pdf][bib]

Optimality and Stability in Non-Convex Smooth Games
Guojun Zhang, Pascal Poupart, Yaoliang Yu, 2022.
[abs][pdf][bib]

SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
Weijing Tang, Jiaqi Ma, Qiaozhu Mei, Ji Zhu, 2022.
[abs][pdf][bib]      [code]

Model Averaging Is Asymptotically Better Than Model Selection For Prediction
Tri M. Le, Bertrand S. Clarke, 2022.
[abs][pdf][bib]

Active Learning for Nonlinear System Identification with Guarantees
Horia Mania, Michael I. Jordan, Benjamin Recht, 2022.
[abs][pdf][bib]

An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
Jaouad Mourtada, Stéphane Gaïffas, 2022.
[abs][pdf][bib]

A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Augusto Fasano, Daniele Durante, 2022.
[abs][pdf][bib]

Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji, Junjie Yang, Yingbin Liang, 2022.
[abs][pdf][bib]

Novel Min-Max Reformulations of Linear Inverse Problems
Mohammed Rayyan Sheriff, Debasish Chatterjee, 2022.
[abs][pdf][bib]

Data-Derived Weak Universal Consistency
Narayana Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski, 2022.
[abs][pdf][bib]

MurTree: Optimal Decision Trees via Dynamic Programming and Search
Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey, 2022.
[abs][pdf][bib]      [code]

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono, Daniel Paulin, Arnaud Doucet, 2022.
[abs][pdf][bib]

On Biased Stochastic Gradient Estimation
Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb, 2022.
[abs][pdf][bib]

Fast and Robust Rank Aggregation against Model Misspecification
Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama, 2022.
[abs][pdf][bib]

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney, 2022.
[abs][pdf][bib]

Evolutionary Variational Optimization of Generative Models
Jakob Drefs, Enrico Guiraud, Jörg Lücke, 2022.
[abs][pdf][bib]      [code]

Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Tomojit Ghosh, Michael Kirby, 2022.
[abs][pdf][bib]      [code]

Universal Approximation in Dropout Neural Networks
Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies, Jaron Sanders, Albert Senen-Cerda, 2022.
[abs][pdf][bib]

Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang, 2022.
[abs][pdf][bib]      [code]

Spatial Multivariate Trees for Big Data Bayesian Regression
Michele Peruzzi, David B. Dunson, 2022.
[abs][pdf][bib]      [code]

TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb, 2022.
[abs][pdf][bib]      [code]

A Stochastic Bundle Method for Interpolation
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar, 2022.
[abs][pdf][bib]      [code]

On Generalizations of Some Distance Based Classifiers for HDLSS Data
Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Ghosh, 2022.
[abs][pdf][bib]

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet, 2022.
[abs][pdf][bib]      [code]

Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems
Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan, 2022.
[abs][pdf][bib]      [code]

Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes
Ali Kara, Serdar Yuksel, 2022.
[abs][pdf][bib]

Interpolating Predictors in High-Dimensional Factor Regression
Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, 2022.
[abs][pdf][bib]

Scaling Laws from the Data Manifold Dimension
Utkarsh Sharma, Jared Kaplan, 2022.
[abs][pdf][bib]      [code]

Deep Learning in Target Space
Michael Fairbank, Spyridon Samothrakis, Luca Citi, 2022.
[abs][pdf][bib]      [code]

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes
Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee, 2022.
[abs][pdf][bib]      [code]

XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou, 2022.
[abs][pdf][bib]      [code]

Empirical Risk Minimization under Random Censorship
Guillaume Ausset, Stephan Clémençon, François Portier, 2022.
[abs][pdf][bib]

Exploiting locality in high-dimensional Factorial hidden Markov models
Lorenzo Rimella, Nick Whiteley, 2022.
[abs][pdf][bib]      [code]

Recovering shared structure from multiple networks with unknown edge distributions
Keith Levin, Asad Lodhia, Elizaveta Levina, 2022.
[abs][pdf][bib]

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions
Shaogao Lv, Heng Lian, 2022.
[abs][pdf][bib]

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Subhabrata Majumdar, George Michailidis, 2022.
[abs][pdf][bib]      [code]

Full list

© JMLR 2022.