JMLR Volume 24

• Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search

  Benjamin Moseley, Joshua R. Wang (1):1−36, 2023 PDF BibTeX

• The Brier Score under Administrative Censoring: Problems and a Solution

  Håvard Kvamme, Ørnulf Borgan (2):1−26, 2023 PDF BibTeX

• Bayesian Spiked Laplacian Graphs

  Leo L Duan, George Michailidis, Mingzhou Ding (3):1−35, 2023 codePDF BibTeX

• Efficient Structure-preserving Support Tensor Train Machine

  Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner (4):1−22, 2023 codePDF BibTeX

• Cluster-Specific Predictions with Multi-Task Gaussian Processes

  Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey (5):1−49, 2023 codePDF BibTeX

• AutoKeras: An AutoML Library for Deep Learning

  Haifeng Jin, François Chollet, Qingquan Song, Xia Hu (6):1−6, 2023 codePDF BibTeX

• On Distance and Kernel Measures of Conditional Dependence

  Tianhong Sheng, Bharath K. Sriperumbudur (7):1−16, 2023 PDF BibTeX

• A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs

  Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong (8):1−37, 2023 PDF BibTeX

• Sampling random graph homomorphisms and applications to network data analysis

  Hanbaek Lyu, Facundo Memoli, David Sivakoff (9):1−79, 2023 codePDF BibTeX

• A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees

  Michael J. O'Neill, Stephen J. Wright (10):1−34, 2023 PDF BibTeX

• Optimal Strategies for Reject Option Classifiers

  Vojtech Franc, Daniel Prusa, Vaclav Voracek (11):1−49, 2023 PDF BibTeX

• Learning-augmented count-min sketches via Bayesian nonparametrics

  Emanuele Dolera, Stefano Favaro, Stefano Peluchetti (12):1−60, 2023 PDF BibTeX

• Adaptation to the Range in K-Armed Bandits

  Hédi Hadiji, Gilles Stoltz (13):1−33, 2023 PDF BibTeX

• Python package for causal discovery based on LiNGAM

  Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu (14):1−8, 2023 codePDF BibTeX

• Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

  Jon Vadillo, Roberto Santana, Jose A. Lozano (15):1−42, 2023 codePDF BibTeX

• Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

  Cynthia Rudin, Yaron Shaposhnik (16):1−44, 2023 codePDF BibTeX

• Learning Mean-Field Games with Discounted and Average Costs

  Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi (17):1−59, 2023 PDF BibTeX

• An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization

  Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis (18):1−41, 2023 codePDF BibTeX

• Regularized Joint Mixture Models

  Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee (19):1−47, 2023 codePDF BibTeX

• Interpolating Classifiers Make Few Mistakes

  Tengyuan Liang, Benjamin Recht (20):1−27, 2023 PDF BibTeX

• Graph-Aided Online Multi-Kernel Learning

  Pouya M. Ghari, Yanning Shen (21):1−44, 2023 codePDF BibTeX

• Lower Bounds and Accelerated Algorithms for Bilevel Optimization

  Kaiyi ji, Yingbin Liang (22):1−56, 2023 PDF BibTeX

• Bayesian Data Selection

  Eli N. Weinstein, Jeffrey W. Miller (23):1−72, 2023 codePDF BibTeX

• Calibrated Multiple-Output Quantile Regression with Representation Learning

  Shai Feldman, Stephen Bates, Yaniv Romano (24):1−48, 2023 codePDF BibTeX

• Discrete Variational Calculus for Accelerated Optimization

  Cédric M. Campos, Alejandro Mahillo, David Martín de Diego (25):1−33, 2023 codePDF BibTeX

• Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels

  Hao Wang, Rui Gao, Flavio P. Calmon (26):1−43, 2023 PDF BibTeX

• The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time

  Raj Agrawal, Tamara Broderick (27):1−60, 2023 PDF BibTeX

• Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping

  XuranMeng, JeffYao (28):1−40, 2023 codePDF BibTeX

• HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn

  Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard (29):1−17, 2023 codePDF BibTeX

• Attacks against Federated Learning Defense Systems and their Mitigation

  Cody Lewis, Vijay Varadharajan, Nasimul Noman (30):1−50, 2023 codePDF BibTeX

• Labels, Information, and Computation: Efficient Learning Using Sufficient Labels

  Shiyu Duan, Spencer Chang, Jose C. Principe (31):1−35, 2023 PDF BibTeX

• Sparse PCA: a Geometric Approach

  Dimitris Bertsimas, Driss Lahlou Kitane (32):1−33, 2023 PDF BibTeX

• Gap Minimization for Knowledge Sharing and Transfer

  Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton (33):1−57, 2023 codePDF BibTeX

• 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 (34):1−11, 2023 codePDF BibTeX

• 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 (35):1−52, 2023 PDF BibTeX

• Label Distribution Changing Learning with Sample Space Expanding

  Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou (36):1−48, 2023 PDF BibTeX

• Ridges, Neural Networks, and the Radon Transform

  Michael Unser (37):1−33, 2023 PDF BibTeX

• First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems

  Michael I. Jordan, Tianyi Lin, Manolis Zampetakis (38):1−46, 2023 PDF BibTeX

• Sensing Theorems for Unsupervised Learning in Linear Inverse Problems

  Julián Tachella, Dongdong Chen, Mike Davies (39):1−45, 2023 PDF BibTeX

• On Batch Teaching Without Collusion

  Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles (40):1−33, 2023 PDF BibTeX

• Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data

  Shaowu Pan, Steven L. Brunton, J. Nathan Kutz (41):1−60, 2023 codePDF BibTeX

• 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 (42):1−63, 2023 codePDF BibTeX

• Benchmarking Graph Neural Networks

  Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson (43):1−48, 2023 codePDF BibTeX

• Robust Load Balancing with Machine Learned Advice

  Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng (44):1−46, 2023 PDF BibTeX

• The multimarginal optimal transport formulation of adversarial multiclass classification

  Nicolás García Trillos, Matt Jacobs, Jakwang Kim (45):1−56, 2023 PDF BibTeX

• The d-Separation Criterion in Categorical Probability

  Tobias Fritz, Andreas Klingler (46):1−49, 2023 PDF BibTeX

• A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering

  Haizi Yu, Igor Mineyev, Lav R. Varshney (47):1−61, 2023 PDF BibTeX

• On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size

  Xiaoyu Wang, Ya-xiang Yuan (48):1−49, 2023 PDF BibTeX

• Reinforcement Learning for Joint Optimization of Multiple Rewards

  Mridul Agarwal, Vaneet Aggarwal (49):1−41, 2023 PDF BibTeX

• Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning

  Linxi Liu, Dangna Li, Wing Hung Wong (50):1−64, 2023 PDF BibTeX

• Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks

  Lingjun Li, Jun Li (51):1−44, 2023 PDF BibTeX

• Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems

  Kunal Pattanayak, Vikram Krishnamurthy (52):1−64, 2023 PDF BibTeX

• VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback

  Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica (53):1−45, 2023 PDF BibTeX

• Contextual Stochastic Block Model: Sharp Thresholds and Contiguity

  Chen Lu, Subhabrata Sen (54):1−34, 2023 PDF BibTeX

• Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches

  Shaogao Lv, Xin He, Junhui Wang (55):1−38, 2023 PDF BibTeX

• On the geometry of Stein variational gradient descent

  Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch (56):1−39, 2023 PDF BibTeX

• Tree-AMP: Compositional Inference with Tree Approximate Message Passing

  Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová (57):1−89, 2023 codePDF BibTeX

• Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval

  Yan Shuo Tan, Roman Vershynin (58):1−47, 2023 PDF BibTeX

• Topological Convolutional Layers for Deep Learning

  Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar Carlsson (59):1−35, 2023 PDF BibTeX

• Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints

  Qinbo Bai, Vaneet Aggarwal, Ather Gattami (60):1−25, 2023 PDF BibTeX

• Density estimation on low-dimensional manifolds: an inflation-deflation approach

  Christian Horvat, Jean-Pascal Pfister (61):1−37, 2023 codePDF BibTeX

• Monotonic Alpha-divergence Minimisation for Variational Inference

  Kamélia Daudel, Randal Douc, François Roueff (62):1−76, 2023 PDF BibTeX

• On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results

  Marcelo Arenas, Pablo Barcelo, Leopoldo Bertossi, Mikael Monet (63):1−58, 2023 PDF BibTeX

• Fundamental limits and algorithms for sparse linear regression with sublinear sparsity

  Lan V. Truong (64):1−49, 2023 codePDF BibTeX

• Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule

  Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu (65):1−37, 2023 codePDF BibTeX

• Posterior Contraction for Deep Gaussian Process Priors

  Gianluca Finocchio, Johannes Schmidt-Hieber (66):1−49, 2023 PDF BibTeX

• Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

  Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami (67):1−51, 2023 codePDF BibTeX

• Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data

  Ruoyu Wang, Miaomiao Su, Qihua Wang (68):1−52, 2023 codePDF BibTeX

• When Locally Linear Embedding Hits Boundary

  Hau-Tieng Wu, Nan Wu (69):1−80, 2023 PDF BibTeX

• Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection

  Jonathan Hillman, Toby Dylan Hocking (70):1−24, 2023 codePDF BibTeX

• Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition

  Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau (71):1−57, 2023 codePDF BibTeX

• How Do You Want Your Greedy: Simultaneous or Repeated?

  Moran Feldman, Christopher Harshaw, Amin Karbasi (72):1−87, 2023 codePDF BibTeX

• Inference for a Large Directed Acyclic Graph with Unspecified Interventions

  Chunlin Li, Xiaotong Shen, Wei Pan (73):1−48, 2023 codePDF BibTeX

• Privacy-Aware Rejection Sampling

  Jordan Awan, Vinayak Rao (74):1−32, 2023 PDF BibTeX

• Intrinsic Persistent Homology via Density-based Metric Learning

  Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman (75):1−42, 2023 codePDF BibTeX

• A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection

  Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath (76):1−31, 2023 PDF BibTeX

• A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models

  Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin (77):1−42, 2023 PDF BibTeX

• Towards Learning to Imitate from a Single Video Demonstration

  Glen Berseth, Florian Golemo, Christopher Pal (78):1−26, 2023 PDF BibTeX

• Approximate Post-Selective Inference for Regression with the Group LASSO

  Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler (79):1−49, 2023 PDF BibTeX

• Temporal Abstraction in Reinforcement Learning with the Successor Representation

  Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling (80):1−69, 2023 PDF BibTeX

• Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics

  Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill (81):1−36, 2023 codePDF BibTeX

• Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality

  Ning Ning, Edward L. Ionides (82):1−76, 2023 PDF BibTeX

• Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

  William J. Wilkinson, Simo Särkkä, Arno Solin (83):1−50, 2023 codePDF BibTeX

• Online Optimization over Riemannian Manifolds

  Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi (84):1−67, 2023 codePDF BibTeX

• Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence

  Henry Lam, Haofeng Zhang (85):1−58, 2023 PDF BibTeX

• Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces

  George Stepaniants (86):1−72, 2023 codePDF BibTeX

• Gaussian Processes with Errors in Variables: Theory and Computation

  Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll (87):1−53, 2023 PDF BibTeX

• Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data

  Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson (88):1−49, 2023 PDF BibTeX

• 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 (89):1−97, 2023 codePDF BibTeX

• Outlier-Robust Subsampling Techniques for Persistent Homology

  Bernadette J. Stolz (90):1−35, 2023 codePDF BibTeX

• Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds

  Likai Chen, Georg Keilbar, Wei Biao Wu (91):1−25, 2023 PDF BibTeX

• Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption

  Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang (92):1−46, 2023 PDF BibTeX

• Decentralized Learning: Theoretical Optimality and Practical Improvements

  Yucheng Lu, Christopher De Sa (93):1−62, 2023 PDF BibTeX

• Faith-Shap: The Faithful Shapley Interaction Index

  Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar (94):1−42, 2023 PDF BibTeX

• Statistical Inference for Noisy Incomplete Binary Matrix

  Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu (95):1−66, 2023 PDF BibTeX

• Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization

  Jianhao Ma, Salar Fattahi (96):1−84, 2023 PDF BibTeX

• Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation

  Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu (97):1−30, 2023 codePDF BibTeX

• An Analysis of Robustness of Non-Lipschitz Networks

  Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang (98):1−43, 2023 codePDF BibTeX

• Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity

  Artem Vysogorets, Julia Kempe (99):1−23, 2023 PDF BibTeX

• FedLab: A Flexible Federated Learning Framework

  Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu (100):1−7, 2023 codePDF BibTeX

• Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds

  Didong Li, Wenpin Tang, Sudipto Banerjee (101):1−26, 2023 PDF BibTeX

• Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments

  Haixu Ma, Donglin Zeng, Yufeng Liu (102):1−48, 2023 PDF BibTeX

• Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint

  Michael R. Metel (103):1−44, 2023 PDF BibTeX

• Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics

  Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang (104):1−42, 2023 PDF BibTeX

• Knowledge Hypergraph Embedding Meets Relational Algebra

  Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole (105):1−34, 2023 codePDF BibTeX

• Concentration analysis of multivariate elliptic diffusions

  Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch (106):1−38, 2023 PDF BibTeX

• Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption

  Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile (107):1−31, 2023 PDF BibTeX

• Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors

  Michail Spitieris, Ingelin Steinsland (108):1−39, 2023 codePDF BibTeX

• Dimensionless machine learning: Imposing exact units equivariance

  Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu (109):1−32, 2023 PDF BibTeX

• A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

  Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi (110):1−43, 2023 codePDF BibTeX

• FLIP: A Utility Preserving Privacy Mechanism for Time Series

  Tucker McElroy, Anindya Roy, Gaurab Hore (111):1−29, 2023 PDF BibTeX

• The Hyperspherical Geometry of Community Detection: Modularity as a Distance

  Martijn Gösgens, Remco van der Hofstad, Nelly Litvak (112):1−36, 2023 codePDF BibTeX

• The Implicit Bias of Benign Overfitting

  Ohad Shamir (113):1−40, 2023 PDF BibTeX

• Generalization Bounds for Adversarial Contrastive Learning

  Xin Zou, Weiwei Liu (114):1−54, 2023 PDF BibTeX

• Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition

  Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang (115):1−53, 2023 PDF BibTeX

• SQLFlow: An Extensible Toolkit Integrating DB and AI

  Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen (116):1−9, 2023 codePDF BibTeX

• Deep linear networks can benignly overfit when shallow ones do

  Niladri S. Chatterji, Philip M. Long (117):1−39, 2023 codePDF BibTeX

• A Unified Framework for Optimization-Based Graph Coarsening

  Manoj Kumar, Anurag Sharma, Sandeep Kumar (118):1−50, 2023 codePDF BibTeX

• An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks

  Stefan Stein, Chenlei Leng (119):1−69, 2023 PDF BibTeX

• Maximum likelihood estimation in Gaussian process regression is ill-posed

  Toni Karvonen, Chris J. Oates (120):1−47, 2023 PDF BibTeX

• Minimal Width for Universal Property of Deep RNN

  Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang (121):1−41, 2023 PDF BibTeX

• Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

  Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock (122):1−77, 2023 PDF BibTeX

• Benign overfitting in ridge regression

  Alexander Tsigler, Peter L. Bartlett (123):1−76, 2023 PDF BibTeX

• HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation

  Weijie J. Su, Yuancheng Zhu (124):1−53, 2023 PDF BibTeX

• Statistical Robustness of Empirical Risks in Machine Learning

  Shaoyan Guo, Huifu Xu, Liwei Zhang (125):1−38, 2023 PDF BibTeX

• Euler-Lagrange Analysis of Generative Adversarial Networks

  Siddarth Asokan, Chandra Sekhar Seelamantula (126):1−100, 2023 codePDF BibTeX

• Graph Clustering with Graph Neural Networks

  Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller (127):1−21, 2023 codePDF BibTeX

• An Eigenmodel for Dynamic Multilayer Networks

  Joshua Daniel Loyal, Yuguo Chen (128):1−69, 2023 codePDF BibTeX

• 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 (129):1−23, 2023 PDF BibTeX

• Combinatorial Optimization and Reasoning with Graph Neural Networks

  Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković (130):1−61, 2023 PDF BibTeX

• A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition

  Patricia Wollstadt, Sebastian Schmitt, Michael Wibral (131):1−44, 2023 PDF BibTeX

• Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data

  Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu (132):1−57, 2023 PDF BibTeX

• Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

  Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill (133):1−57, 2023 codePDF BibTeX

• MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation

  Qian Li, Binyan Jiang, Defeng Sun (134):1−44, 2023 PDF BibTeX

• Sparse GCA and Thresholded Gradient Descent

  Sheng Gao, Zongming Ma (135):1−61, 2023 PDF BibTeX

• Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection

  Wenhao Li, Ningyuan Chen, L. Jeff Hong (136):1−84, 2023 PDF BibTeX

• Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks

  Hui Jin, Guido Montufar (137):1−97, 2023 codePDF BibTeX

• Asymptotics of Network Embeddings Learned via Subsampling

  Andrew Davison, Morgane Austern (138):1−120, 2023 codePDF BibTeX

• Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games

  Ben Hambly, Renyuan Xu, Huining Yang (139):1−56, 2023 PDF BibTeX

• Jump Interval-Learning for Individualized Decision Making with Continuous Treatments

  Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu (140):1−92, 2023 codePDF BibTeX

• Optimal Convergence Rates for Distributed Nystroem Approximation

  Jian Li, Yong Liu, Weiping Wang (141):1−39, 2023 codePDF BibTeX

• On Tilted Losses in Machine Learning: Theory and Applications

  Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith (142):1−79, 2023 codePDF BibTeX

• Large sample spectral analysis of graph-based multi-manifold clustering

  Nicolas Garcia Trillos, Pengfei He, Chenghui Li (143):1−71, 2023 codePDF BibTeX

• Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering

  Noirrit Kiran Chandra, Antonio Canale, David B. Dunson (144):1−42, 2023 PDF BibTeX

• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning

  Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang (145):1−46, 2023 codePDF BibTeX

• Off-Policy Actor-Critic with Emphatic Weightings

  Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White (146):1−63, 2023 codePDF BibTeX

• Stochastic Optimization under Distributional Drift

  Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui (147):1−56, 2023 PDF BibTeX

• Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition

  Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang (148):1−63, 2023 PDF BibTeX

• Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning

  Titouan Vayer, Rémi Gribonval (149):1−51, 2023 PDF BibTeX

• 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 (150):1−12, 2023 codePDF BibTeX

• Generalization error bounds for multiclass sparse linear classifiers

  Tomer Levy, Felix Abramovich (151):1−35, 2023 PDF BibTeX

• Selective inference for k-means clustering

  Yiqun T. Chen, Daniela M. Witten (152):1−41, 2023 codePDF BibTeX

• Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process

  Cheng Zeng, Jeffrey W Miller, Leo L Duan (153):1−32, 2023 codePDF BibTeX

• Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees

  Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd (154):1−48, 2023 codePDF BibTeX

• Adaptive Data Depth via Multi-Armed Bandits

  Tavor Baharav, Tze Leung Lai (155):1−29, 2023 PDF BibTeX

• Integrating Random Effects in Deep Neural Networks

  Giora Simchoni, Saharon Rosset (156):1−57, 2023 codePDF BibTeX

• Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity

  Huan Li, Zhouchen Lin (157):1−37, 2023 codePDF BibTeX

• Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees

  Hamid Reza Feyzmahdavian, Mikael Johansson (158):1−75, 2023 PDF BibTeX

• Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations

  Arnab Ganguly, Riten Mitra, Jinpu Zhou (159):1−39, 2023 PDF BibTeX

• Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling

  Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron (160):1−65, 2023 codePDF BibTeX

• q-Learning in Continuous Time

  Yanwei Jia, Xun Yu Zhou (161):1−61, 2023 codePDF BibTeX

• Flexible Model Aggregation for Quantile Regression

  Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani (162):1−45, 2023 codePDF BibTeX

• Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification

  Gavin Zhang, Salar Fattahi, Richard Y. Zhang (163):1−55, 2023 PDF BibTeX

• A Framework and Benchmark for Deep Batch Active Learning for Regression

  David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart (164):1−81, 2023 codePDF BibTeX

• Robust Methods for High-Dimensional Linear Learning

  Ibrahim Merad, Stéphane Gaïffas (165):1−44, 2023 PDF BibTeX

• A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition

  Masaru Ito, Zhaosong Lu, Chuan He (166):1−34, 2023 PDF BibTeX

• Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start

  Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo (167):1−37, 2023 codePDF BibTeX

• Inference on the Change Point under a High Dimensional Covariance Shift

  Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis (168):1−68, 2023 PDF BibTeX

• DART: Distance Assisted Recursive Testing

  Xuechan Li, Anthony D. Sung, Jichun Xie (169):1−41, 2023 PDF BibTeX

• Small Transformers Compute Universal Metric Embeddings

  Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić (170):1−48, 2023 codePDF BibTeX

• Incremental Learning in Diagonal Linear Networks

  Raphaël Berthier (171):1−26, 2023 PDF BibTeX

• Beyond the Golden Ratio for Variational Inequality Algorithms

  Ahmet Alacaoglu, Axel Böhm, Yura Malitsky (172):1−33, 2023 codePDF BibTeX

• From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions

  Johannes Resin (173):1−21, 2023 PDF BibTeX

• Posterior Consistency for Bayesian Relevance Vector Machines

  Xiao Fang, Malay Ghosh (174):1−17, 2023 PDF BibTeX

• Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

  Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang (175):1−53, 2023 PDF BibTeX

• Evaluating Instrument Validity using the Principle of Independent Mechanisms

  Patrick F. Burauel (176):1−56, 2023 PDF BibTeX

• Comprehensive Algorithm Portfolio Evaluation using Item Response Theory

  Sevvandi Kandanaarachchi, Kate Smith-Miles (177):1−52, 2023 codePDF BibTeX

• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

  Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha (178):1−75, 2023 PDF BibTeX

• Variational Inference for Deblending Crowded Starfields

  Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, The LSST Dark Energy Science Collaboration (179):1−36, 2023 codePDF BibTeX

• Dropout Training is Distributionally Robust Optimal

  José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang (180):1−60, 2023 PDF BibTeX

• Factor Graph Neural Networks

  Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee (181):1−54, 2023 codePDF BibTeX

• Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding

  Justin Grimmer, Dean Knox, Brandon Stewart (182):1−70, 2023 PDF BibTeX

• Quasi-Equivalence between Width and Depth of Neural Networks

  Fenglei Fan, Rongjie Lai, Ge Wang (183):1−22, 2023 PDF BibTeX

• Metrizing Weak Convergence with Maximum Mean Discrepancies

  Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey (184):1−20, 2023 PDF BibTeX

• On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity

  Rodrigue Siry, Ryan Webster, Loic Simon, Julien Rabin (185):1−34, 2023 PDF BibTeX

• Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks

  Jun Shu, Deyu Meng, Zongben Xu (186):1−74, 2023 codePDF BibTeX

• Quantifying Network Similarity using Graph Cumulants

  Gecia Bravo-Hermsdorff, Lee M. Gunderson, Pierre-André Maugis, Carey E. Priebe (187):1−27, 2023 codePDF BibTeX

• The Proximal ID Algorithm

  Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen (188):1−46, 2023 codePDF BibTeX

• Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality

  Lukas Gonon (189):1−51, 2023 PDF BibTeX

• Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees

  Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg (190):1−56, 2023 codePDF BibTeX

• Insights into Ordinal Embedding Algorithms: A Systematic Evaluation

  Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg (191):1−83, 2023 codePDF BibTeX

• PAC-learning for Strategic Classification

  Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao (192):1−38, 2023 PDF BibTeX

• Divide-and-Conquer Fusion

  Ryan S.Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts (193):1−82, 2023 PDF BibTeX

• MMD Aggregated Two-Sample Test

  Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton (194):1−81, 2023 codePDF BibTeX

• Clustering and Structural Robustness in Causal Diagrams

  Santtu Tikka, Jouni Helske, Juha Karvanen (195):1−32, 2023 codePDF BibTeX

• Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data

  Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann (196):1−72, 2023 codePDF BibTeX

• Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency

  Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum (197):1−65, 2023 codePDF BibTeX

• CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges

  Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu (198):1−6, 2023 codePDF BibTeX

• Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity

  Ali Kara, Naci Saldi, Serdar Yüksel (199):1−34, 2023 PDF BibTeX

• Model-based Causal Discovery for Zero-Inflated Count Data

  Junsouk Choi, Yang Ni (200):1−32, 2023 codePDF BibTeX

• Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations

  Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng (201):1−60, 2023 PDF BibTeX

• Multiplayer Performative Prediction: Learning in Decision-Dependent Games

  Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff (202):1−56, 2023 codePDF BibTeX

• A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points

  Lili Su, Jiaming Xu, Pengkun Yang (203):1−48, 2023 PDF BibTeX

• Buffered Asynchronous SGD for Byzantine Learning

  Yi-Rui Yang, Wu-Jun Li (204):1−62, 2023 PDF BibTeX

• L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

  Hussein Hazimeh, Rahul Mazumder, Tim Nonet (205):1−8, 2023 codePDF BibTeX

• Non-stationary Online Learning with Memory and Non-stochastic Control

  Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou (206):1−70, 2023 PDF BibTeX

• Augmented Sparsifiers for Generalized Hypergraph Cuts

  Nate Veldt, Austin R. Benson, Jon Kleinberg (207):1−50, 2023 codePDF BibTeX

• Minimax Risk Classifiers with 0-1 Loss

  Santiago Mazuelas, Mauricio Romero, Peter Grunwald (208):1−48, 2023 PDF BibTeX

• LibMTL: A Python Library for Deep Multi-Task Learning

  Baijiong Lin, Yu Zhang (209):1−7, 2023 codePDF BibTeX

• GFlowNet Foundations

  Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio (210):1−55, 2023 PDF BibTeX

• Entropic Fictitious Play for Mean Field Optimization Problem

  Fan Chen, Zhenjie Ren, Songbo Wang (211):1−36, 2023 PDF BibTeX

• An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity

  Wei Liu, Xin Liu, Xiaojun Chen (212):1−43, 2023 PDF BibTeX

• Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

  Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz (213):1−45, 2023 codePDF BibTeX

• An Empirical Investigation of the Role of Pre-training in Lifelong Learning

  Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell (214):1−50, 2023 codePDF BibTeX

• Least Squares Model Averaging for Distributed Data

  Haili Zhang, Zhaobo Liu, Guohua Zou (215):1−59, 2023 PDF BibTeX

• Random Forests for Change Point Detection

  Malte Londschien, Peter Bühlmann, Solt Kovács (216):1−45, 2023 codePDF BibTeX

• GANs as Gradient Flows that Converge

  Yu-Jui Huang, Yuchong Zhang (217):1−40, 2023 PDF BibTeX

• Adaptation Augmented Model-based Policy Optimization

  Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang (218):1−35, 2023 PDF BibTeX

• Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data

  Hua Liu, Jinhong You, Jiguo Cao (219):1−41, 2023 codePDF BibTeX

• A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

  Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee (220):1−32, 2023 codePDF BibTeX

• Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices

  Doudou Zhou, Tianxi Cai, Junwei Lu (221):1−43, 2023 codePDF BibTeX

• Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees

  Mo Zhou, Jianfeng Lu (222):1−34, 2023 codePDF BibTeX

• Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data

  Bingqing Hu, Bin Nan (223):1−26, 2023 codePDF BibTeX

• RankSEG: A Consistent Ranking-based Framework for Segmentation

  Ben Dai, Chunlin Li (224):1−50, 2023 codePDF BibTeX

• Limits of Dense Simplicial Complexes

  T. Mitchell Roddenberry, Santiago Segarra (225):1−42, 2023 PDF BibTeX

• Merlion: End-to-End Machine Learning for Time Series

  Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang (226):1−6, 2023 codePDF BibTeX

• Autoregressive Networks

  Binyan Jiang, Jialiang Li, Qiwei Yao (227):1−69, 2023 PDF BibTeX

• On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure

  Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon (228):1−38, 2023 PDF BibTeX

• Interpretable and Fair Boolean Rule Sets via Column Generation

  Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei (229):1−50, 2023 PDF BibTeX

• Sample Complexity for Distributionally Robust Learning under chi-square divergence

  Zhengyu Zhou, Weiwei Liu (230):1−27, 2023 PDF BibTeX

• Statistical Comparisons of Classifiers by Generalized Stochastic Dominance

  Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin (231):1−37, 2023 PDF BibTeX

• Lifted Bregman Training of Neural Networks

  Xiaoyu Wang, Martin Benning (232):1−51, 2023 codePDF BibTeX

• Strategic Knowledge Transfer

  Max Olan Smith, Thomas Anthony, Michael P. Wellman (233):1−96, 2023 PDF BibTeX

• MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning

  Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov (234):1−7, 2023 codePDF BibTeX

• Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models

  Ziyue Wang, Zhiqiang Tan (235):1−112, 2023 codePDF BibTeX

• Neural Q-learning for solving PDEs

  Samuel N. Cohen, Deqing Jiang, Justin Sirignano (236):1−49, 2023 codePDF BibTeX

• Scalable Computation of Causal Bounds

  Madhumitha Shridharan, Garud Iyengar (237):1−35, 2023 PDF BibTeX

• Efficient Computation of Rankings from Pairwise Comparisons

  M. E. J. Newman (238):1−25, 2023 PDF BibTeX

• Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification

  Oh-Ran Kwon, Hui Zou (239):1−40, 2023 codePDF BibTeX

• PaLM: Scaling Language Modeling with Pathways

  Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel (240):1−113, 2023 PDF BibTeX

• Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning

  Zhuang Yang (241):1−29, 2023 PDF BibTeX

• Sparse Graph Learning from Spatiotemporal Time Series

  Andrea Cini, Daniele Zambon, Cesare Alippi (242):1−36, 2023 PDF BibTeX

• Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics

  Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet (243):1−83, 2023 PDF BibTeX

• Selection by Prediction with Conformal p-values

  Ying Jin, Emmanuel J. Candes (244):1−41, 2023 codePDF BibTeX

• Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing

  Yibo Yan, Xiaozhou Wang, Riquan Zhang (245):1−49, 2023 PDF BibTeX

• Graph Attention Retrospective

  Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath (246):1−52, 2023 codePDF BibTeX

• Importance Sparsification for Sinkhorn Algorithm

  Mengyu Li, Jun Yu, Tao Li, Cheng Meng (247):1−44, 2023 codePDF BibTeX

• Improving multiple-try Metropolis with local balancing

  Philippe Gagnon, Florian Maire, Giacomo Zanella (248):1−59, 2023 PDF BibTeX

• Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC

  Tianze Wang, Guanyang Wang (249):1−40, 2023 codePDF BibTeX

• Convex Reinforcement Learning in Finite Trials

  Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli (250):1−42, 2023 PDF BibTeX

• Atlas: Few-shot Learning with Retrieval Augmented Language Models

  Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave (251):1−43, 2023 codePDF BibTeX

• Adaptive False Discovery Rate Control with Privacy Guarantee

  Xintao Xia, Zhanrui Cai (252):1−35, 2023 PDF BibTeX

• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

  Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat (253):1−15, 2023 codePDF BibTeX

• skrl: Modular and Flexible Library for Reinforcement Learning

  Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba (254):1−9, 2023 codePDF BibTeX

• Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures

  Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum (255):1−10, 2023 codePDF BibTeX

• Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

  Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White (256):1−34, 2023 codePDF BibTeX

• Fairlearn: Assessing and Improving Fairness of AI Systems

  Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio (257):1−8, 2023 codePDF BibTeX

• Multi-view Collaborative Gaussian Process Dynamical Systems

  Shiliang Sun, Jingjing Fei, Jing Zhao, Liang Mao (258):1−32, 2023 PDF BibTeX

• Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance

  Ray Bai, Mary R. Boland, Yong Chen (259):1−49, 2023 codePDF BibTeX

• Learning to Rank under Multinomial Logit Choice

  James A. Grant, David S. Leslie (260):1−49, 2023 PDF BibTeX

• Nearest Neighbor Dirichlet Mixtures

  Shounak Chattopadhyay, Antik Chakraborty, David B. Dunson (261):1−46, 2023 codePDF BibTeX

• Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?

  Shuxiao Chen, Qinqing Zheng, Qi Long, Weijie J. Su (262):1−59, 2023 PDF BibTeX

• Distributed Algorithms for U-statistics-based Empirical Risk Minimization

  Lanjue Chen, Alan T.K. Wan, Shuyi Zhang, Yong Zhou (263):1−43, 2023 PDF BibTeX

• ProtoryNet - Interpretable Text Classification Via Prototype Trajectories

  Dat Hong, Tong Wang, Stephen Baek (264):1−39, 2023 codePDF BibTeX

• Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction

  Jue Hou, Zijian Guo, Tianxi Cai (265):1−58, 2023 PDF BibTeX

• On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators

  Zejian Liu, Meng Li (266):1−37, 2023 PDF BibTeX

• Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

  Dimitris Bertsimas, Ryan Cory-Wright, Nicholas A. G. Johnson (267):1−51, 2023 codePDF BibTeX

• Revisiting minimum description length complexity in overparameterized models

  Raaz Dwivedi, Chandan Singh, Bin Yu, Martin Wainwright (268):1−59, 2023 codePDF BibTeX

• Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method

  Eglantine Karlé, Hemant Tyagi (269):1−57, 2023 codePDF BibTeX

• Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation

  Xiao-Tong Yuan, Ping Li (270):1−52, 2023 PDF BibTeX

• Causal Discovery with Unobserved Confounding and Non-Gaussian Data

  Y. Samuel Wang, Mathias Drton (271):1−61, 2023 PDF BibTeX

• Distributed Sparse Regression via Penalization

  Yao Ji, Gesualdo Scutari, Ying Sun, Harsha Honnappa (272):1−62, 2023 PDF BibTeX

• Online Non-stochastic Control with Partial Feedback

  Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou (273):1−50, 2023 PDF BibTeX

• A Continuous-time Stochastic Gradient Descent Method for Continuous Data

  Kexin Jin, Jonas Latz, Chenguang Liu, Carola-Bibiane Schönlieb (274):1−48, 2023 PDF BibTeX

• Adaptive Clustering Using Kernel Density Estimators

  Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann (275):1−56, 2023 PDF BibTeX

• On Biased Compression for Distributed Learning

  Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan (276):1−50, 2023 PDF BibTeX

• Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net

  Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten (277):1−53, 2023 codePDF BibTeX

• Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation

  Christoph Käding,, Jakob Runge, (278):1−144, 2023 PDF BibTeX

• Sparse Markov Models for High-dimensional Inference

  Guilherme Ost, Daniel Y. Takahashi (279):1−54, 2023 PDF BibTeX

• Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD

  Kun Yuan, Sulaiman A. Alghunaim, Xinmeng Huang (280):1−53, 2023 PDF BibTeX

• The Bayesian Learning Rule

  Mohammad Emtiyaz Khan, Håvard Rue (281):1−46, 2023 PDF BibTeX

• Community models for networks observed through edge nominations

  Tianxi Li, Elizaveta Levina, Ji Zhu (282):1−36, 2023 codePDF BibTeX

• Near-Optimal Weighted Matrix Completion

  Oscar López (283):1−40, 2023 PDF BibTeX

• A Complete Characterization of Linear Estimators for Offline Policy Evaluation

  Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade (284):1−50, 2023 PDF BibTeX

• Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables

  Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke (285):1−59, 2023 codePDF BibTeX

• Low Tree-Rank Bayesian Vector Autoregression Models

  Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang (286):1−35, 2023 codePDF BibTeX

• Universal Approximation Property of Invertible Neural Networks

  Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama (287):1−68, 2023 PDF BibTeX

• A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits

  Yasin Abbasi-Yadkori, András György, Nevena Lazić (288):1−37, 2023 PDF BibTeX

• Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility

  Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, Francois Caron (289):1−78, 2023 codePDF BibTeX

• Deletion and Insertion Tests in Regression Models

  Naofumi Hama, Masayoshi Mase, Art B. Owen (290):1−38, 2023 PDF BibTeX

• A Unified Analysis of Multi-task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty

  Shiyuan He, Hanxuan Ye, Kejun He (291):1−69, 2023 PDF BibTeX

• From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms

  Weijie Zheng, Benjamin Doerr (292):1−40, 2023 PDF BibTeX

• Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models

  Molei Liu, Yi Zhang, Katherine P. Liao, Tianxi Cai (293):1−50, 2023 PDF BibTeX

• Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

  Louis-Philippe Vignault, Audrey Durand, Pascal Germain (294):1−13, 2023 PDF BibTeX

• Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-Varying Covariates

  Quan Zhang, Yanxun Xu, Mei-Cheng Wang, Mingyuan Zhou (295):1−43, 2023 PDF BibTeX

• High-Dimensional Inference for Generalized Linear Models with Hidden Confounding

  Jing Ouyang, Kean Ming Tan, Gongjun Xu (296):1−61, 2023 PDF BibTeX

• Causal Bandits for Linear Structural Equation Models

  Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer (297):1−59, 2023 PDF BibTeX

• A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning

  Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, Stefano V. Albrecht (298):1−74, 2023 codePDF BibTeX

• A PDE approach for regret bounds under partial monitoring

  Erhan Bayraktar, Ibrahim Ekren, Xin Zhang (299):1−24, 2023 PDF BibTeX

• Sensitivity-Free Gradient Descent Algorithms

  Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, John Maxwell (300):1−26, 2023 PDF BibTeX

• Learning Optimal Feedback Operators and their Sparse Polynomial Approximations

  Karl Kunisch, Donato Vásquez-Varas, Daniel Walter (301):1−38, 2023 PDF BibTeX

• Pivotal Estimation of Linear Discriminant Analysis in High Dimensions

  Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao (302):1−45, 2023 PDF BibTeX

• Random Feature Amplification: Feature Learning and Generalization in Neural Networks

  Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett (303):1−49, 2023 PDF BibTeX

• Two Sample Testing in High Dimension via Maximum Mean Discrepancy

  Hanjia Gao, Xiaofeng Shao (304):1−33, 2023 PDF BibTeX

• Continuous-in-time Limit for Bayesian Bandits

  Yuhua Zhu, Zachary Izzo, Lexing Ying (305):1−35, 2023 PDF BibTeX

• Multi-Consensus Decentralized Accelerated Gradient Descent

  Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang (306):1−50, 2023 PDF BibTeX

• Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation

  Guillaume Sagnol, Luc Pronzato (307):1−32, 2023 codePDF BibTeX

• Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

  Jorg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuan Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc’Aurelio Ranzato (308):1−77, 2023 codePDF BibTeX

• Dimension Reduction and MARS

  Yu Liu LIU, Degui Li, Yingcun Xia (309):1−30, 2023 PDF BibTeX

• Prediction Equilibrium for Dynamic Network Flows

  Lukas Graf, Tobias Harks, Kostas Kollias, Michael Markl (310):1−33, 2023 codePDF BibTeX

• Microcanonical Hamiltonian Monte Carlo

  Jakob Robnik, G. Bruno De Luca, Eva Silverstein, Uroš Seljak (311):1−34, 2023 codePDF BibTeX

• The Measure and Mismeasure of Fairness

  Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan, Ravi Shroff, Sharad Goel (312):1−117, 2023 codePDF BibTeX

• Zeroth-Order Alternating Gradient Descent Ascent Algorithms for A Class of Nonconvex-Nonconcave Minimax Problems

  Zi Xu, Zi-Qi Wang, Jun-Lin Wang, Yu-Hong Dai (313):1−25, 2023 PDF BibTeX

• Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression

  Jackson Zhou, John T. Ormerod, Clara Grazian (314):1−39, 2023 codePDF BibTeX

• MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

  Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang (315):1−23, 2023 codePDF BibTeX

• The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima

  Peter L. Bartlett, Philip M. Long, Olivier Bousquet (316):1−36, 2023 PDF BibTeX

• Mixed Regression via Approximate Message Passing

  Nelvin Tan, Ramji Venkataramanan (317):1−44, 2023 codePDF BibTeX

• Operator learning with PCA-Net: upper and lower complexity bounds

  Samuel Lanthaler (318):1−67, 2023 PDF BibTeX

• Bagging in overparameterized learning: Risk characterization and risk monotonization

  Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla (319):1−113, 2023 PDF BibTeX

• Higher-Order Spectral Clustering Under Superimposed Stochastic Block Models

  Subhadeep Paul, Olgica Milenkovic, Yuguo Chen (320):1−58, 2023 PDF BibTeX

• Scale Invariant Power Iteration

  Cheolmin Kim, Youngseok Kim, Diego Klabjan (321):1−47, 2023 codePDF BibTeX

• Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks

  Simge Kucukyavuz, Ali Shojaie, Hasan Manzour, Linchuan Wei, Hao-Hsiang Wu (322):1−38, 2023 PDF BibTeX

• Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes

  Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai, Rajarshi Mukherjee (323):1−86, 2023 codePDF BibTeX

• Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders

  Lisa Bonheme, Marek Grzes (324):1−30, 2023 codePDF BibTeX

• ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI

  Samuel Hess, Gregory Ditzler (325):1−49, 2023 codePDF BibTeX

• Benign Overfitting of Constant-Stepsize SGD for Linear Regression

  Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade (326):1−58, 2023 PDF BibTeX

• Reproducing Kernels and New Approaches in Compositional Data Analysis

  Binglin Li, Changwon Yoon, Jeongyoun Ahn (327):1−34, 2023 PDF BibTeX

• Bandit problems with fidelity rewards

  Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle (328):1−44, 2023 PDF BibTeX

• Mini-batching error and adaptive Langevin dynamics

  Inass Sekkat, Gabriel Stoltz (329):1−58, 2023 PDF BibTeX

• The Power of Contrast for Feature Learning: A Theoretical Analysis

  Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang (330):1−78, 2023 PDF BibTeX

• Fair Data Representation for Machine Learning at the Pareto Frontier

  Shizhou Xu, Thomas Strohmer (331):1−63, 2023 codePDF BibTeX

• Learning Conditional Generative Models for Phase Retrieval

  Tobias Uelwer, Sebastian Konietzny, Alexander Oberstrass, Stefan Harmeling (332):1−28, 2023 PDF BibTeX

• Weisfeiler and Leman go Machine Learning: The Story so far

  Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt (333):1−59, 2023 PDF BibTeX

• Dimensionality Reduction and Wasserstein Stability for Kernel Regression

  Stephan Eckstein, Armin Iske, Mathias Trabs (334):1−35, 2023 PDF BibTeX

• T-Cal: An Optimal Test for the Calibration of Predictive Models

  Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban (335):1−72, 2023 codePDF BibTeX

• Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters

  Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares (336):1−35, 2023 PDF BibTeX

• The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension

  Seonghyun Jeong, Veronika Rockova (337):1−65, 2023 PDF BibTeX

• Community Recovery in the Geometric Block Model

  Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha (338):1−53, 2023 PDF BibTeX

• Compression, Generalization and Learning

  Marco C. Campi, Simone Garatti (339):1−74, 2023 PDF BibTeX

• Topological Hidden Markov Models

  Adam B Kashlak, Prachi Loliencar, Giseon Heo (340):1−49, 2023 codePDF BibTeX

• A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets

  Jacques Wainer (341):1−34, 2023 codePDF BibTeX

• The Geometry and Calculus of Losses

  Robert C. Williamson, Zac Cranko (342):1−72, 2023 PDF BibTeX

• Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems

  You Zhao, Xiaofeng Liao, Xing He, Mingliang Zhou, Chaojie Li (343):1−59, 2023 PDF BibTeX

• Large data limit of the MBO scheme for data clustering: convergence of the dynamics

  Tim Laux, Jona Lelmi (344):1−49, 2023 PDF BibTeX

• Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning

  John Harlim, Shixiao Willing Jiang, John Wilson Peoples (345):1−85, 2023 PDF BibTeX

• Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications

  Johannes Kirschner, Tor Lattimore, Andreas Krause (346):1−45, 2023 PDF BibTeX

• Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion

  Haifeng Wang, Jinchi Chen, Ke Wei (347):1−84, 2023 PDF BibTeX

• Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries

  Matteo Sesia, Stefano Favaro, Edgar Dobriban (348):1−80, 2023 codePDF BibTeX

• Instance-Dependent Generalization Bounds via Optimal Transport

  Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Andreas Krause, Jonas Rothfuss (349):1−51, 2023 PDF BibTeX

• Robust High-Dimensional Low-Rank Matrix Estimation: Optimal Rate and Data-Adaptive Tuning

  Xiaolong Cui, Lei Shi, Wei Zhong, Changliang Zou (350):1−57, 2023 PDF BibTeX

• Modular Regression: Improving Linear Models by Incorporating Auxiliary Data

  Ying Jin, Dominik Rothenhäusler (351):1−52, 2023 PDF BibTeX

• Group SLOPE Penalized Low-Rank Tensor Regression

  Yang Chen, Ziyan Luo (352):1−30, 2023 PDF BibTeX

• Limitations on approximation by deep and shallow neural networks

  Guergana Petrova, Przemyslaw Wojtaszczyk (353):1−38, 2023 PDF BibTeX

• A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

  Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash (354):1−31, 2023 codePDF BibTeX

• Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

  Thijs Vogels, Hadrien Hendrikx, Martin Jaggi (355):1−31, 2023 codePDF BibTeX

• MAUVE Scores for Generative Models: Theory and Practice

  Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui (356):1−92, 2023 codePDF BibTeX

• Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces

  Jonathan W. Siegel (357):1−52, 2023 PDF BibTeX

• Optimal Parameter-Transfer Learning by Semiparametric Model Averaging

  Xiaonan Hu, Xinyu Zhang (358):1−53, 2023 PDF BibTeX

• A Unified Theory of Diversity in Ensemble Learning

  Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown (359):1−49, 2023 codePDF BibTeX

• Attribution-based Explanations that Provide Recourse Cannot be Robust

  Hidde Fokkema, Rianne de Heide, Tim van Erven (360):1−37, 2023 codePDF BibTeX

• Differentially Private Hypothesis Testing for Linear Regression

  Daniel G. Alabi, Salil P. Vadhan (361):1−50, 2023 PDF BibTeX

• Discovering Salient Neurons in deep NLP models

  Nadir Durrani, Fahim Dalvi, Hassan Sajjad (362):1−40, 2023 codePDF BibTeX

• Avalanche: A PyTorch Library for Deep Continual Learning

  Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco (363):1−6, 2023 codePDF BibTeX

• Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

  Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand (364):1−50, 2023 codePDF BibTeX

• Hard-Constrained Deep Learning for Climate Downscaling

  Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell Watson, David Rolnick (365):1−40, 2023 codePDF BibTeX

• Confidence and Uncertainty Assessment for Distributional Random Forests

  Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen (366):1−77, 2023 codePDF BibTeX

• TorchOpt: An Efficient Library for Differentiable Optimization

  Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang (367):1−14, 2023 codePDF BibTeX

• LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery

  Paul Maria Scheikl, Balázs Gyenes, Rayan Younis, Christoph Haas, Gerhard Neumann, Martin Wagner, Franziska Mathis-Ullrich (368):1−42, 2023 codePDF BibTeX

• A Permutation-Free Kernel Independence Test

  Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas (369):1−68, 2023 codePDF BibTeX

• Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations

  Devanshu Agrawal, James Ostrowski (370):1−40, 2023 codePDF BibTeX

• Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty

  Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou (371):1−40, 2023 PDF BibTeX

• A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds

  Ben Chugg, Hongjian Wang, Aaditya Ramdas (372):1−61, 2023 PDF BibTeX

• Multilevel CNNs for Parametric PDEs

  Cosmas Heiß, Ingo Gühring, Martin Eigel (373):1−42, 2023 PDF BibTeX

• Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling

  Stefano Peluchetti (374):1−51, 2023 codePDF BibTeX

• Set-valued Classification with Out-of-distribution Detection for Many Classes

  Zhou Wang, Xingye Qiao (375):1−39, 2023 codePDF BibTeX

• On the Dynamics Under the Unhinged Loss and Beyond

  Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Jiangjunjun, Xiangyang Ji (376):1−62, 2023 PDF BibTeX

• Scaling Up Models and Data with t5x and seqio

  Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo (377):1−8, 2023 codePDF BibTeX

• Principled Out-of-Distribution Detection via Multiple Testing

  Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy, Susmit Jha (378):1−35, 2023 PDF BibTeX

• On Learning Rates and Schrödinger Operators

  Bin Shi, Weijie Su, Michael I. Jordan (379):1−53, 2023 PDF BibTeX

• Randomized Spectral Co-Clustering for Large-Scale Directed Networks

  Xiao Guo, Yixuan Qiu, Hai Zhang, Xiangyu Chang (380):1−68, 2023 codePDF BibTeX

• Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence

  Yuetian Luo, Anru R. Zhang (381):1−48, 2023 codePDF BibTeX

• A Novel Integer Linear Programming Approach for Global L0 Minimization

  Diego Delle Donne, Matthieu Kowalski, Leo Liberti (382):1−28, 2023 PDF BibTeX

• Over-parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning

  Xingdong Feng, Yuling Jiao, Lican Kang, Baqun Zhang, Fan Zhou (383):1−40, 2023 PDF BibTeX

• On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error

  Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen (384):1−41, 2023 PDF BibTeX

• Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning

  Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang, Mengdi Wang (385):1−43, 2023 PDF BibTeX

• Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

  Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause (386):1−62, 2023 PDF BibTeX

• Distributed Statistical Inference under Heterogeneity

  Jia Gu, Song Xi Chen (387):1−57, 2023 PDF BibTeX

• Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

  Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar (388):1−26, 2023 codePDF BibTeX

• Semiparametric Inference Using Fractional Posteriors

  Alice L'Huillier, Luke Travis, Ismaël Castillo, Kolyan Ray (389):1−61, 2023 PDF BibTeX

• A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers

  Nahuel Statuto, Irene Unceta, Jordi Nin, Oriol Pujol (390):1−34, 2023 PDF BibTeX

• Hierarchical Kernels in Deep Kernel Learning

  Wentao Huang, Houbao Lu, Haizhang Zhang (391):1−30, 2023 codePDF BibTeX

• Instance-Dependent Confidence and Early Stopping for Reinforcement Learning

  Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan (392):1−43, 2023 PDF BibTeX

• A Unified Approach to Controlling Implicit Regularization via Mirror Descent

  Haoyuan Sun, Khashayar Gatmiry, Kwangjun Ahn, Navid Azizan (393):1−58, 2023 PDF BibTeX

• Revisiting inference after prediction

  Keshav Motwani, Daniela Witten (394):1−18, 2023 codePDF BibTeX

• Adaptive Learning of Density Ratios in RKHS

  Werner Zellinger, Stefan Kindermann, Sergei V. Pereverzyev (395):1−28, 2023 PDF BibTeX

• RVCL: Evaluating the Robustness of Contrastive Learning via Verification

  Zekai Wang, Weiwei Liu (396):1−43, 2023 codePDF BibTeX

• Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph

  Leo L. Duan, David B. Dunson (397):1−44, 2023 codePDF BibTeX

• Finding Groups of Cross-Correlated Features in Bi-View Data

  Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew B. Nobel (398):1−47, 2023 codePDF BibTeX

• Boosting Multi-agent Reinforcement Learning via Contextual Prompting

  Yue Deng, Zirui Wang, Xi Chen, Yin Zhang (399):1−34, 2023 PDF BibTeX

• Foundation Models and Fair Use

  Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang (400):1−79, 2023 PDF BibTeX

• Distributed Community Detection in Large Networks

  Sheng Zhang, Rui Song, Wenbin Lu, Ji Zhu (401):1−28, 2023 PDF BibTeX