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

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• 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

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• 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

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• Small Transformers Compute Universal Metric Embeddings

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

• Incremental Learning in Diagonal Linear Networks

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• 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

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• Posterior Consistency for Bayesian Relevance Vector Machines

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• Evaluating Instrument Validity using the Principle of Independent Mechanisms

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• Comprehensive Algorithm Portfolio Evaluation using Item Response Theory

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• 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

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• Dropout Training is Distributionally Robust Optimal

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

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• Quantifying Network Similarity using Graph Cumulants

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  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

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• Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees

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• Insights into Ordinal Embedding Algorithms: A Systematic Evaluation

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

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• Random Forests for Change Point Detection

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

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  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

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  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

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

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  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

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

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

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