JMLR Volume 25

On Truthing Issues in Supervised Classification: Jonathan K. Su; (1):1−91, 2024.
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Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction: Yuze Han, Guangzeng Xie, Zhihua Zhang; (2):1−86, 2024.
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Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic: Zheng Tracy Ke, Jun S. Liu, Yucong Ma; (3):1−67, 2024.
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Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization: Shicong Cen, Yuting Wei, Yuejie Chi; (4):1−48, 2024.
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Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method: Ernesto Araya, Guillaume Braun, Hemant Tyagi; (5):1−43, 2024.
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Model-Free Representation Learning and Exploration in Low-Rank MDPs: Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal; (6):1−76, 2024.
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Decorrelated Variable Importance: Isabella Verdinelli, Larry Wasserman; (7):1−27, 2024.
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On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models: Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh; (8):1−46, 2024.
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Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment: Zixian Yang, Xin Liu, Lei Ying; (9):1−55, 2024.
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Modeling Random Networks with Heterogeneous Reciprocity: Daniel Cirkovic, Tiandong Wang; (10):1−40, 2024.
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Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design: Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov; (11):1−37, 2024.
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Critically Assessing the State of the Art in Neural Network Verification: Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn; (12):1−53, 2024.
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A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent: Stefan Ankirchner, Stefan Perko; (13):1−55, 2024.
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Improving physics-informed neural networks with meta-learned optimization: Alex Bihlo; (14):1−26, 2024.
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On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks: Sebastian Neumayer, Lénaïc Chizat, Michael Unser; (15):1−24, 2024.
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Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond: Nathan Kallus, Xiaojie Mao, Masatoshi Uehara; (16):1−59, 2024.
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On Sufficient Graphical Models: Bing Li, Kyongwon Kim; (17):1−64, 2024.
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Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box: Ryan Giordano, Martin Ingram, Tamara Broderick; (18):1−39, 2024.
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Nonparametric Inference under B-bits Quantization: Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang; (19):1−68, 2024.
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Iterate Averaging in the Quest for Best Test Error: Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts; (20):1−55, 2024.
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Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension: Shotaro Yagishita, Jun-ya Gotoh; (21):1−42, 2024.
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On the Generalization of Stochastic Gradient Descent with Momentum: Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang; (22):1−56, 2024.
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Post-Regularization Confidence Bands for Ordinary Differential Equations: Xiaowu Dai, Lexin Li; (23):1−51, 2024.
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Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces: Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao; (24):1−67, 2024.
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On Tail Decay Rate Estimation of Loss Function Distributions: Etrit Haxholli, Marco Lorenzi; (25):1−47, 2024.
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Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees: Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge; (26):1−36, 2024.
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Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality: Stephan Wojtowytsch; (27):1−49, 2024.
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Additive smoothing error in backward variational inference for general state-space models: Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff; (28):1−33, 2024.
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Rates of convergence for density estimation with generative adversarial networks: Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov; (29):1−47, 2024.
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Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent: Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi; (30):1−27, 2024.
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Sample-efficient Adversarial Imitation Learning: Dahuin Jung, Hyungyu Lee, Sungroh Yoon; (31):1−32, 2024.
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Heterogeneous-Agent Reinforcement Learning: Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang; (32):1−67, 2024.
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Pygmtools: A Python Graph Matching Toolkit: Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan; (33):1−7, 2024. (Machine Learning Open Source Software Paper)
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Effect-Invariant Mechanisms for Policy Generalization: Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters; (34):1−36, 2024.
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Deep Network Approximation: Beyond ReLU to Diverse Activation Functions: Shijun Zhang, Jianfeng Lu, Hongkai Zhao; (35):1−39, 2024.
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Sparse NMF with Archetypal Regularization: Computational and Robustness Properties: Kayhan Behdin, Rahul Mazumder; (36):1−62, 2024.
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Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms: T. Tony Cai, Hongji Wei; (37):1−63, 2024.
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Convergence for nonconvex ADMM, with applications to CT imaging: Rina Foygel Barber, Emil Y. Sidky; (38):1−46, 2024.
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On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control: Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel; (39):1−58, 2024.
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Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee: George H. Chen; (40):1−78, 2024.
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Personalized PCA: Decoupling Shared and Unique Features: Naichen Shi, Raed Al Kontar; (41):1−82, 2024.
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Invariant and Equivariant Reynolds Networks: Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai; (42):1−36, 2024. (Machine Learning Open Source Software Paper)
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Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling: Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu; (43):1−44, 2024.
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Multiple Descent in the Multiple Random Feature Model: Xuran Meng, Jianfeng Yao, Yuan Cao; (44):1−49, 2024.
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Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization: Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta; (45):1−64, 2024.
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A Multilabel Classification Framework for Approximate Nearest Neighbor Search: Ville Hyvönen, Elias Jääsaari, Teemu Roos; (46):1−51, 2024.
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Efficient Modality Selection in Multimodal Learning: Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao; (47):1−39, 2024.
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Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees: Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh; (48):1−53, 2024.
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Trained Transformers Learn Linear Models In-Context: Ruiqi Zhang, Spencer Frei, Peter L. Bartlett; (49):1−55, 2024.
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Resource-Efficient Neural Networks for Embedded Systems: Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani; (50):1−51, 2024.
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Optimal First-Order Algorithms as a Function of Inequalities: Chanwoo Park, Ernest K. Ryu; (51):1−66, 2024.
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Axiomatic effect propagation in structural causal models: Raghav Singal, George Michailidis; (52):1−71, 2024.
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Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks: Dong-Young Lim, Sotirios Sabanis; (53):1−52, 2024.
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Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization: Daniel LeJeune, Jiayu Liu, Reinhard Heckel; (54):1−37, 2024.
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Revisiting RIP Guarantees for Sketching Operators on Mixture Models: Ayoub Belhadji, Rémi Gribonval; (55):1−68, 2024.
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A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity: Jiang Hu, Kangkang Deng, Jiayuan Wu, Quanzheng Li; (56):1−32, 2024.
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Data Thinning for Convolution-Closed Distributions: Anna Neufeld, Ameer Dharamshi, Lucy L. Gao, Daniela Witten; (57):1−35, 2024.
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Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification: Natalie S. Frank, Jonathan Niles-Weed; (58):1−41, 2024.
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Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics: Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles; (59):1−44, 2024.
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Causal-learn: Causal Discovery in Python: Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang; (60):1−8, 2024. (Machine Learning Open Source Software Paper)
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Scaling the Convex Barrier with Sparse Dual Algorithms: Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar; (61):1−51, 2024.
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Low-rank Variational Bayes correction to the Laplace method: Janet van Niekerk, Haavard Rue; (62):1−25, 2024.
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An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates: Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner; (63):1−60, 2024.
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Mathematical Framework for Online Social Media Auditing: Wasim Huleihel, Yehonathan Refael; (64):1−40, 2024.
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Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions: Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra, Dimitris Perdios, Sebastian Neumayer, Michael Unser; (65):1−30, 2024.
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On Unbiased Estimation for Partially Observed Diffusions: Jeremy Heng, Jeremie Houssineau, Ajay Jasra; (66):1−66, 2024. (Machine Learning Open Source Software Paper)
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Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning: Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman; (67):1−31, 2024.
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Learnability of Linear Port-Hamiltonian Systems: Juan-Pablo Ortega, Daiying Yin; (68):1−56, 2024.
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Tangential Wasserstein Projections: Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee; (69):1−41, 2024.
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Scaling Instruction-Finetuned Language Models: Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei; (70):1−53, 2024.
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Policy Gradient Methods in the Presence of Symmetries and State Abstractions: Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup; (71):1−57, 2024.
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Pareto Smoothed Importance Sampling: Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry; (72):1−58, 2024.
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Data Summarization via Bilevel Optimization: Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause; (73):1−53, 2024.
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Differentially private methods for managing model uncertainty in linear regression: Víctor Peña, Andrés F. Barrientos; (74):1−44, 2024.
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Towards Explainable Evaluation Metrics for Machine Translation: Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva, Wei Zhao, Yang Gao, Steffen Eger; (75):1−49, 2024.
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Distributed Estimation on Semi-Supervised Generalized Linear Model: Jiyuan Tu, Weidong Liu, Xiaojun Mao; (76):1−41, 2024.
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Unlabeled Principal Component Analysis and Matrix Completion: Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris; (77):1−38, 2024.
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Functional Directed Acyclic Graphs: Kuang-Yao Lee, Lexin Li, Bing Li; (78):1−48, 2024.
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Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria: Victor Bystrov, Viktoriia Naboka-Krell, Anna Staszewska-Bystrova, Peter Winker; (79):1−30, 2024.
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ptwt - The PyTorch Wavelet Toolbox: Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt; (80):1−7, 2024. (Machine Learning Open Source Software Paper)
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Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions: Maksim Velikanov, Dmitry Yarotsky; (81):1−78, 2024.
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On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains: Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin; (82):1−47, 2024.
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Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training: Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan; (83):1−74, 2024.
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On the Learnability of Out-of-distribution Detection: Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu; (84):1−83, 2024.
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Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport: Ricardo Baptista, Youssef Marzouk, Rebecca Morrison, Olivier Zahm; (85):1−46, 2024.
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A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables: Wei Luo, Yeying Zhu, Xuekui Zhang, Lin Lin; (86):1−38, 2024.
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Spatial meshing for general Bayesian multivariate models: Michele Peruzzi, David B. Dunson; (87):1−49, 2024.
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Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks: Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang; (88):1−75, 2024.
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Minimax Rates for High-Dimensional Random Tessellation Forests: Eliza O'Reilly, Ngoc Mai Tran; (89):1−32, 2024.
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Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality: Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy; (90):1−49, 2024.
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The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective: Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar; (91):1−85, 2024.
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Exploration of the Search Space of Gaussian Graphical Models for Paired Data: Alberto Roverato, Dung Ngoc Nguyen; (92):1−41, 2024.
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Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces: Rui Wang, Yuesheng Xu, Mingsong Yan; (93):1−45, 2024.
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Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent: Jiaming Xu, Hanjing Zhu; (94):1−83, 2024.
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A General Framework for the Analysis of Kernel-based Tests: Tamara Fernández, Nicolás Rivera; (95):1−40, 2024.
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MAP- and MLE-Based Teaching: Hans Ulrich Simon, Jan Arne Telle; (96):1−34, 2024.
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Scaling Speech Technology to 1,000+ Languages: Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli; (97):1−52, 2024.
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Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization: Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou; (98):1−52, 2024.
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Semi-supervised Inference for Block-wise Missing Data without Imputation: Shanshan Song, Yuanyuan Lin, Yong Zhou; (99):1−36, 2024.
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Materials Discovery using Max K-Armed Bandit: Nobuaki Kikkawa, Hiroshi Ohno; (100):1−40, 2024.
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AMLB: an AutoML Benchmark: Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren; (101):1−65, 2024.
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Nonparametric Regression for 3D Point Cloud Learning: Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai; (102):1−56, 2024.
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Information Processing Equalities and the Information–Risk Bridge: Robert C. Williamson, Zac Cranko; (103):1−53, 2024.
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Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data: Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini; (104):1−47, 2024.
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Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?: Roel Bouman, Zaharah Bukhsh, Tom Heskes; (105):1−34, 2024.
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PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design: Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick; (106):1−26, 2024.
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Random Forest Weighted Local Fréchet Regression with Random Objects: Rui Qiu, Zhou Yu, Ruoqing Zhu; (107):1−69, 2024.
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QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration: Felix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully; (108):1−16, 2024. (Machine Learning Open Source Software Paper)
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Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space: Zhengdao Chen; (109):1−65, 2024.
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More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity: Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund; (110):1−43, 2024.
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