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.
News
- 2023.01.20: Volume 23 completed; Volume 24 began.
- 2022.07.20: New special issue on climate change.
- 2022.02.18: New blog post: Retrospectives from 20 Years of JMLR .
- 2022.01.25: Volume 22 completed; Volume 23 began.
- 2021.12.02: Message from outgoing co-EiC Bernhard Schölkopf.
- 2021.02.10: Volume 21 completed; Volume 22 began.
- More news ...
Latest papers
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From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions
Johannes Resin, 2023
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Beyond the Golden Ratio for Variational Inequality Algorithms
Ahmet Alacaoglu, Axel Böhm, Yura Malitsky, 2023
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Incremental Learning in Diagonal Linear Networks
Raphaël Berthier, 2023
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Small Transformers Compute Universal Metric Embeddings
Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić, 2023
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DART: Distance Assisted Recursive Testing
Xuechan Li, Anthony D. Sung, Jichun Xie, 2023
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Inference on the Change Point under a High Dimensional Covariance Shift
Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis, 2023
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Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, 2023
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A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition
Masaru Ito, Zhaosong Lu, Chuan He, 2023
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Robust Methods for High-Dimensional Linear Learning
Ibrahim Merad, Stéphane Gaïffas, 2023
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A Framework and Benchmark for Deep Batch Active Learning for Regression
David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart, 2023
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Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
Gavin Zhang, Salar Fattahi, Richard Y. Zhang, 2023
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Flexible Model Aggregation for Quantile Regression
Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani, 2023
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q-Learning in Continuous Time
Yanwei Jia, Xun Yu Zhou, 2023
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Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling
Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron, 2023
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Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations
Arnab Ganguly, Riten Mitra, Jinpu Zhou, 2023
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Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
Hamid Reza Feyzmahdavian, Mikael Johansson, 2023
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Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity
Huan Li, Zhouchen Lin, 2023
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Integrating Random Effects in Deep Neural Networks
Giora Simchoni, Saharon Rosset, 2023
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Adaptive Data Depth via Multi-Armed Bandits
Tavor Baharav, Tze Leung Lai, 2023
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Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees
Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd, 2023
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Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process
Cheng Zeng, Jeffrey W Miller, Leo L Duan, 2023
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Selective inference for k-means clustering
Yiqun T. Chen, Daniela M. Witten, 2023
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Generalization error bounds for multiclass sparse linear classifiers
Tomer Levy, Felix Abramovich, 2023
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MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang, 2023
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Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
Titouan Vayer, Rémi Gribonval, 2023
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Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang, 2023
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Stochastic Optimization under Distributional Drift
Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui, 2023
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Off-Policy Actor-Critic with Emphatic Weightings
Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White, 2023
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Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang, 2023
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Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering
Noirrit Kiran Chandra, Antonio Canale, David B. Dunson, 2023
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Large sample spectral analysis of graph-based multi-manifold clustering
Nicolas Garcia Trillos, Pengfei He, Chenghui Li, 2023
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On Tilted Losses in Machine Learning: Theory and Applications
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith, 2023
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Optimal Convergence Rates for Distributed Nystroem Approximation
Jian Li, Yong Liu, Weiping Wang, 2023
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Jump Interval-Learning for Individualized Decision Making with Continuous Treatments
Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu, 2023
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Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
Ben Hambly, Renyuan Xu, Huining Yang, 2023
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Asymptotics of Network Embeddings Learned via Subsampling
Andrew Davison, Morgane Austern, 2023
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Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks
Hui Jin, Guido Montufar, 2023
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Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
Wenhao Li, Ningyuan Chen, L. Jeff Hong, 2023
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Sparse GCA and Thresholded Gradient Descent
Sheng Gao, Zongming Ma, 2023
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MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation
Qian Li, Binyan Jiang, Defeng Sun, 2023
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Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill, 2023
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Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data
Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu, 2023
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A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral, 2023
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Combinatorial Optimization and Reasoning with Graph Neural Networks
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic, 2023
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A First Look into the Carbon Footprint of Federated Learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane, 2023
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An Eigenmodel for Dynamic Multilayer Networks
Joshua Daniel Loyal, Yuguo Chen, 2023
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Graph Clustering with Graph Neural Networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller, 2023
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Euler-Lagrange Analysis of Generative Adversarial Networks
Siddarth Asokan, Chandra Sekhar Seelamantula, 2023
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Statistical Robustness of Empirical Risks in Machine Learning
Shaoyan Guo, Huifu Xu, Liwei Zhang, 2023
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HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Weijie J. Su, Yuancheng Zhu, 2023
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Benign overfitting in ridge regression
Alexander Tsigler, Peter L. Bartlett, 2023
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Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock, 2023
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Minimal Width for Universal Property of Deep RNN
Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang, 2023
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Maximum likelihood estimation in Gaussian process regression is ill-posed
Toni Karvonen, Chris J. Oates, 2023
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An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks
Stefan Stein, Chenlei Leng, 2023
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A Unified Framework for Optimization-Based Graph Coarsening
Manoj Kumar, Anurag Sharma, Sandeep Kumar, 2023
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Deep linear networks can benignly overfit when shallow ones do
Niladri S. Chatterji, Philip M. Long, 2023
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SQLFlow: An Extensible Toolkit Integrating DB and AI
Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen, 2023
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Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition
Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang, 2023
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Generalization Bounds for Adversarial Contrastive Learning
Xin Zou, Weiwei Liu, 2023
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The Implicit Bias of Benign Overfitting
Ohad Shamir, 2023
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The Hyperspherical Geometry of Community Detection: Modularity as a Distance
Martijn Gösgens, Remco van der Hofstad, Nelly Litvak, 2023
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FLIP: A Utility Preserving Privacy Mechanism for Time Series
Tucker McElroy, Anindya Roy, Gaurab Hore, 2023
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A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi, 2023
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Dimensionless machine learning: Imposing exact units equivariance
Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu, 2023
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Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors
Michail Spitieris, Ingelin Steinsland, 2023
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Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption
Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile, 2023
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Concentration analysis of multivariate elliptic diffusions
Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch, 2023
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Knowledge Hypergraph Embedding Meets Relational Algebra
Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole, 2023
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Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang, 2023
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Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint
Michael R. Metel, 2023
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Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
Haixu Ma, Donglin Zeng, Yufeng Liu, 2023
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Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
Didong Li, Wenpin Tang, Sudipto Banerjee, 2023
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FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu, 2023
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Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
Artem Vysogorets, Julia Kempe, 2023
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An Analysis of Robustness of Non-Lipschitz Networks
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang, 2023
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Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation
Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu, 2023
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Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization
Jianhao Ma, Salar Fattahi, 2023
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Statistical Inference for Noisy Incomplete Binary Matrix
Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu, 2023
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Faith-Shap: The Faithful Shapley Interaction Index
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar, 2023
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Decentralized Learning: Theoretical Optimality and Practical Improvements
Yucheng Lu, Christopher De Sa, 2023
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Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang, 2023
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Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds
Likai Chen, Georg Keilbar, Wei Biao Wu, 2023
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Outlier-Robust Subsampling Techniques for Persistent Homology
Bernadette J. Stolz, 2023
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Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs
Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar, 2023
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Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson, 2023
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Gaussian Processes with Errors in Variables: Theory and Computation
Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll, 2023
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Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
George Stepaniants, 2023
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Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence
Henry Lam, Haofeng Zhang, 2023
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Online Optimization over Riemannian Manifolds
Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi, 2023
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Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
William J. Wilkinson, Simo Särkkä, Arno Solin, 2023
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Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality
Ning Ning, Edward L. Ionides, 2023
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Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics
Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill, 2023
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Temporal Abstraction in Reinforcement Learning with the Successor Representation
Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling, 2023
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Approximate Post-Selective Inference for Regression with the Group LASSO
Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler, 2023
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Towards Learning to Imitate from a Single Video Demonstration
Glen Berseth, Florian Golemo, Christopher Pal, 2023
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A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin, 2023
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A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection
Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath, 2023
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Intrinsic Persistent Homology via Density-based Metric Learning
Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman, 2023
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Privacy-Aware Rejection Sampling
Jordan Awan, Vinayak Rao, 2023
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Inference for a Large Directed Acyclic Graph with Unspecified Interventions
Chunlin Li, Xiaotong Shen, Wei Pan, 2023
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How Do You Want Your Greedy: Simultaneous or Repeated?
Moran Feldman, Christopher Harshaw, Amin Karbasi, 2023
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Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau, 2023
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Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection
Jonathan Hillman, Toby Dylan Hocking, 2023
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When Locally Linear Embedding Hits Boundary
Hau-Tieng Wu, Nan Wu, 2023
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Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data
Ruoyu Wang, Miaomiao Su, Qihua Wang, 2023
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Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami, 2023
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Posterior Contraction for Deep Gaussian Process Priors
Gianluca Finocchio, Johannes Schmidt-Hieber, 2023
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Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu, 2023
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Fundamental limits and algorithms for sparse linear regression with sublinear sparsity
Lan V. Truong, 2023
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On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
Marcelo Arenas, Pablo Barcelo, Leopoldo Bertossi, Mikael Monet, 2023
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Monotonic Alpha-divergence Minimisation for Variational Inference
Kamélia Daudel, Randal Douc, François Roueff, 2023
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Density estimation on low-dimensional manifolds: an inflation-deflation approach
Christian Horvat, Jean-Pascal Pfister, 2023
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Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints
Qinbo Bai, Vaneet Aggarwal, Ather Gattami, 2023
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Topological Convolutional Layers for Deep Learning
Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar Carlsson, 2023
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Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval
Yan Shuo Tan, Roman Vershynin, 2023
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Tree-AMP: Compositional Inference with Tree Approximate Message Passing
Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová, 2023
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On the geometry of Stein variational gradient descent
Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch, 2023
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Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
Shaogao Lv, Xin He, Junhui Wang, 2023
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Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
Chen Lu, Subhabrata Sen, 2023
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VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica, 2023
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Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems
Kunal Pattanayak, Vikram Krishnamurthy, 2023
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Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks
Lingjun Li, Jun Li, 2023
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Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning
Linxi Liu, Dangna Li, Wing Hung Wong, 2023
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Reinforcement Learning for Joint Optimization of Multiple Rewards
Mridul Agarwal, Vaneet Aggarwal, 2023
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On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size
Xiaoyu Wang, Ya-xiang Yuan, 2023
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A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering
Haizi Yu, Igor Mineyev, Lav R. Varshney, 2023
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The d-Separation Criterion in Categorical Probability
Tobias Fritz, Andreas Klingler, 2023
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The multimarginal optimal transport formulation of adversarial multiclass classification
Nicolás García Trillos, Matt Jacobs, Jakwang Kim, 2023
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Robust Load Balancing with Machine Learned Advice
Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng, 2023
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Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson, 2023
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A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan, 2023
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Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz, 2023
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On Batch Teaching Without Collusion
Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles, 2023
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Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
Julián Tachella, Dongdong Chen, Mike Davies, 2023
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First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis, 2023
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Ridges, Neural Networks, and the Radon Transform
Michael Unser, 2023
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Label Distribution Changing Learning with Sample Space Expanding
Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou, 2023
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Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?
Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan, 2023
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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne, 2023
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Gap Minimization for Knowledge Sharing and Transfer
Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton, 2023
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Sparse PCA: a Geometric Approach
Dimitris Bertsimas, Driss Lahlou Kitane, 2023
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Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Shiyu Duan, Spencer Chang, Jose C. Principe, 2023
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Attacks against Federated Learning Defense Systems and their Mitigation
Cody Lewis, Vijay Varadharajan, Nasimul Noman, 2023
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HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn
Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard, 2023
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Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
XuranMeng, JeffYao, 2023
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The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Raj Agrawal, Tamara Broderick, 2023
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Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Hao Wang, Rui Gao, Flavio P. Calmon, 2023
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Discrete Variational Calculus for Accelerated Optimization
Cédric M. Campos, Alejandro Mahillo, David Martín de Diego, 2023
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Calibrated Multiple-Output Quantile Regression with Representation Learning
Shai Feldman, Stephen Bates, Yaniv Romano, 2023
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Bayesian Data Selection
Eli N. Weinstein, Jeffrey W. Miller, 2023
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Lower Bounds and Accelerated Algorithms for Bilevel Optimization
Kaiyi ji, Yingbin Liang, 2023
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Graph-Aided Online Multi-Kernel Learning
Pouya M. Ghari, Yanning Shen, 2023
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Interpolating Classifiers Make Few Mistakes
Tengyuan Liang, Benjamin Recht, 2023
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Regularized Joint Mixture Models
Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee, 2023
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An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization
Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis, 2023
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Learning Mean-Field Games with Discounted and Average Costs
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi, 2023
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Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
Cynthia Rudin, Yaron Shaposhnik, 2023
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Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A. Lozano, 2023
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Python package for causal discovery based on LiNGAM
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu, 2023
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Adaptation to the Range in K-Armed Bandits
Hédi Hadiji, Gilles Stoltz, 2023
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Learning-augmented count-min sketches via Bayesian nonparametrics
Emanuele Dolera, Stefano Favaro, Stefano Peluchetti, 2023
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Optimal Strategies for Reject Option Classifiers
Vojtech Franc, Daniel Prusa, Vaclav Voracek, 2023
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A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
Michael J. O'Neill, Stephen J. Wright, 2023
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Sampling random graph homomorphisms and applications to network data analysis
Hanbaek Lyu, Facundo Memoli, David Sivakoff, 2023
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A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs
Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong, 2023
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On Distance and Kernel Measures of Conditional Dependence
Tianhong Sheng, Bharath K. Sriperumbudur, 2023
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AutoKeras: An AutoML Library for Deep Learning
Haifeng Jin, François Chollet, Qingquan Song, Xia Hu, 2023
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Cluster-Specific Predictions with Multi-Task Gaussian Processes
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey, 2023
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Efficient Structure-preserving Support Tensor Train Machine
Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner, 2023
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Bayesian Spiked Laplacian Graphs
Leo L Duan, George Michailidis, Mingzhou Ding, 2023
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The Brier Score under Administrative Censoring: Problems and a Solution
Håvard Kvamme, Ørnulf Borgan, 2023
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Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
Benjamin Moseley, Joshua R. Wang, 2023
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