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JMLR Volume 27

The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks: Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs; (1):1−50, 2026.
[abs][pdf][bib] [code]

Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?: Ansgar Steland; (2):1−50, 2026.
[abs][pdf][bib]

Efficient frequent directions algorithms for approximate decomposition of matrices and higher-order tensors: Maolin Che, Yimin Wei, Hong Yan; (3):1−56, 2026.
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Identifying Weight-Variant Latent Causal Models: Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi; (4):1−49, 2026.
[abs][pdf][bib] [code]

Classification Under Local Differential Privacy with Model Reversal and Model Averaging: Caihong Qin, Yang Bai; (5):1−44, 2026.
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Stochastic Gradient Methods: Bias, Stability and Generalization: Shuang Zeng, Yunwen Lei; (6):1−55, 2026.
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Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation: Bohan Wu, David M. Blei; (7):1−68, 2026.
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skwdro: a library for Wasserstein distributionally robust machine learning: Florian Vincent, Waïss Azizian, Franck Iutzeler, Jérôme Malick; (8):1−7, 2026. (Machine Learning Open Source Software Paper)
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Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent: Tong Wu; (9):1−90, 2026.
[abs][pdf][bib] [code]

A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation: Chenghao Li, Yuanyuan Lin; (10):1−47, 2026.
[abs][pdf][bib] [code]

Nonlocal Techniques for the Analysis of Deep ReLU Neural Network Approximations: Cornelia Schneider, Mario Ullrich, Jan Vybíral; (11):1−41, 2026.
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Nonlinear function-on-function regression by RKHS: Peijun Sang, Bing Li; (12):1−54, 2026.
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UQLM: A Python Package for Uncertainty Quantification in Large Language Models: Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik, Ho-Kyeong Ra, Viren Bajaj, Zeya Ahmad; (13):1−10, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design: Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan; (14):1−55, 2026.
[abs][pdf][bib] [code]

Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence: Siming Zheng, Guohao Shen, Yuanyuan Lin, Jian Huang; (15):1−60, 2026.
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Flexible Functional Treatment Effect Estimation: Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang, Kwun Chuen Gary Chan; (16):1−48, 2026.
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Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs: Mehrzad Saremi; (17):1−53, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification: Aleksi Avela, Pauliina Ilmonen; (18):1−28, 2026.
[abs][pdf][bib] [code]

An Anytime Algorithm for Good Arm Identification: Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda; (19):1−90, 2026.
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Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation: Terrance D. Savitsky, Julie Gershunskaya; (20):1−32, 2026.
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Learning Bayesian Network Classifiers to Minimize Class Variable Parameters: Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno; (21):1−41, 2026.
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Nonparametric Estimation of a Factorizable Density using Diffusion Models: Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae; (22):1−125, 2026.
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The Distribution of Ridgeless Least Squares Interpolators: Qiyang Han, Xiaocong Xu; (23):1−94, 2026.
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LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport: Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, Omar Ghattas; (24):1−71, 2026.
[abs][pdf][bib] [code]

A Common Interface for Automatic Differentiation: Guillaume Dalle, Adrian Hill; (25):1−13, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Refined Risk Bounds for Unbounded Losses via Transductive Priors: Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy; (26):1−64, 2026.
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Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models: Zijian Guo, Wei Yuan, Cunhui Zhang; (27):1−79, 2026.
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Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information: Miaomiao Yu, Zhongfeng Jiang, Jiaxuan Li, Yong Zhou; (28):1−39, 2026.
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Generative Bayesian Inference with GANs: Yuexi Wang, Veronika Rockova; (29):1−48, 2026.
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Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling: Yudong Wang, Zhi-Sheng Ye, Cheng Yong Tang; (30):1−63, 2026.
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Persistence Diagrams Estimation of Multivariate Piecewise Hölder-continuous Signals: Hugo Henneuse; (31):1−55, 2026.
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CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration: Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret; (32):1−62, 2026.
[abs][pdf][bib] [code]

Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection: Steven Adams, Andrea Patanè, Morteza Lahijanian, Luca Laurenti; (33):1−52, 2026.
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Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width: Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou; (34):1−35, 2026.
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Adaptive Forward Stepwise: A Method for High Sparsity Regression: Ivy Zhang, Robert Tibshirani; (35):1−24, 2026.
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Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection: Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan; (36):1−61, 2026.
[abs][pdf][bib] [code]

Hierarchical Causal Models: Eli N. Weinstein, David M. Blei; (37):1−73, 2026.
[abs][pdf][bib] [code]

Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent: Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou; (38):1−51, 2026.
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Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization: Yan Li, Defeng Sun, Liping Zhang; (39):1−44, 2026.
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A causal fused lasso for interpretable heterogeneous treatment effects estimation: Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla, Gabriel Ruiz; (40):1−56, 2026.
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Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood: Jiangrong Ouyang, Mingming Gong, Howard Bondell; (41):1−28, 2026.
[abs][pdf][bib] [code]

Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization: Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu; (42):1−77, 2026.
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Two-way Node Popularity Model for Directed and Bipartite Networks: Bing-Yi Jing, Ting Li, Jiangzhou Wang, Ya Wang; (43):1−73, 2026.
[abs][pdf][bib] [code]

A Symplectic Analysis of Alternating Mirror Descent: Jonas E. Katona, Xiuyuan Wang, Andre Wibisono; (44):1−61, 2026.
[abs][pdf][bib] [code]

Contrasting Local and Global Modeling with Machine Learning and Satellite Data: A Case Study Estimating Tree Canopy Height in African Savannas: Esther Rolf, Lucia Gordon, Milind Tambe, Andrew Davies; (45):1−37, 2026.
[abs][pdf][bib] [code]

Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models: Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister; (46):1−57, 2026.
[abs][pdf][bib] [code]

DCatalyst: A Unified Accelerated Framework for Decentralized Optimization: TIanyu Cao, Xiaokai Chen, Gesualdo Scutari; (47):1−57, 2026.
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Covariate-dependent Hierarchical Dirichlet Processes: Huizi Zhang, Sara Wade, Natalia Bochkina; (48):1−99, 2026.
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Online Bernstein-von Mises theorem: Jeyong Lee, Junhyeok Choi, Minwoo Chae; (49):1−124, 2026.
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Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective: Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan; (50):1−34, 2026.
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The Role of Contextual Information in Best Arm Identification: Masahiro Kato, Kaito Ariu; (51):1−61, 2026.
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A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks: Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna; (52):1−67, 2026.
[abs][pdf][bib]

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework: Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao; (53):1−76, 2026.
[abs][pdf][bib] [code]

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation: Luyang Fang, Haoran Lu, Yongkai Chen, Wenxuan Zhong, Ping Ma; (54):1−38, 2026.
[abs][pdf][bib] [code]

Neural Exploitation and Exploration of Contextual Bandits: Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He; (55):1−38, 2026.
[abs][pdf][bib]

Causal Influences over Social Learning Networks: Mert Kayaalp, Ali H. Sayed; (56):1−54, 2026.
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Do We Need to Penalize Variance of Losses for Learning with Label Noise?: Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu; (57):1−38, 2026.
[abs][pdf][bib]

Nonparametric generative modeling for time series via Schrödinger bridge: Mohamed Hamdouche, Pierre Henry-Labordère, Huyên Pham; (58):1−23, 2026.
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Sparse Topic Modeling via Spectral Decomposition and Thresholding: Huy Tran, Yating Liu, Claire Donnat; (59):1−76, 2026.
[abs][pdf][bib] [code]

Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula: David Huk, Rilwan A. Adewoyin, Ritabrata Dutta; (60):1−46, 2026.
[abs][pdf][bib] [code]

Global Fréchet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data: Álvaro Gajardo, Hans-Georg Müller; (61):1−49, 2026.
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A Convex Framework for Confounding Robust Inference: Kei Ishikawa, Niao He, Takafumi Kanamori; (62):1−53, 2026.
[abs][pdf][bib] [code]

Kernel-based Distributed Learning Beyond Least Squares: Heng Lian, Xu Guo; (63):1−28, 2026.
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Why "Classic" Transformers Are Shallow and A Depth-Enabling Technique: Yueyao Yu, Yin Zhang; (64):1−32, 2026.
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Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing: Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban; (65):1−87, 2026.
[abs][pdf][bib] [code]

Investigating the Histogram Loss in Regression: Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White; (66):1−54, 2026.
[abs][pdf][bib] [code]

Optimal Approximation and Generalization Errors for Deep Convolutional Neural Networks: Jinxin Wang, Shao-Bo Lin; (67):1−22, 2026.
[abs][pdf][bib] [code]

Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria: Ali D. Kara, Serdar Yüksel; (68):1−50, 2026.
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Minimax density estimation in the adversarial framework under local differential privacy: Mélisande Albert, Juliette Chevallier, Béatrice Laurent, Ousmane Sacko; (69):1−43, 2026.
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A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization: Yuchen Zhu, Yufeng Zhang, Zhaoran Wang, Zhuoran Yang, Xiaohong Chen; (70):1−62, 2026.
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Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies: Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien; (71):1−90, 2026.
[abs][pdf][bib] [code]

Corruptions of Supervised Learning Problems: Typology and Mitigations: Laura Iacovissi, Nan Lu, Robert C. Williamson; (72):1−73, 2026.
[abs][pdf][bib]

Differentially Private Best-Arm Identification: Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu; (73):1−85, 2026.
[abs][pdf][bib] [code]

Vector-Valued Gaussian Processes for Approximating Divergence- or Rotation-free Vector Fields: Quoc Thong Le Gia, Ian Hugh Sloan, Holger Wendland; (74):1−36, 2026.
[abs][pdf][bib]

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent: Adrien Schertzer, Loucas Pillaud-Vivien; (75):1−42, 2026.
[abs][pdf][bib]

A Fully Parameter-Free Second-Order Algorithm for Convex-Concave Minimax Problems: Jun-Lin Wang, Zi Xu, Hui-Ling Zhang; (76):1−32, 2026.
[abs][pdf][bib]

Limiting Over-Smoothing and Over-Squashing of Graph Message Passing by Deep Scattering Transforms: Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang, Yu Guang Wang; (77):1−39, 2026.
[abs][pdf][bib] [code]

Multi-relational Network Autoregression Model with Latent Group Structures: Yimeng Ren, Xuening Zhu, Ganggang Xu, Yanyuan Ma; (78):1−135, 2026.
[abs][pdf][bib] [code]

Enhancing Accuracy in Generative Models via Knowledge Transfer: Xinyu Tian, Xiaotong Shen; (79):1−58, 2026.
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Demographic Parity in Regression and Classification Within the Unawareness Framework: Vincent Divol, Solenne Gaucher; (80):1−42, 2026.
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Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control: Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang; (81):1−54, 2026.
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Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data: Wanli Hong, Shuyang Ling; (82):1−46, 2026.
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Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models: Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu; (83):1−78, 2026.
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Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features: Jiuqi Wang, Shangtong Zhang; (84):1−36, 2026.
[abs][pdf][bib]

Convergence of Noise-Free Sampling Algorithms with Regularized Wasserstein Proximals: Fuqun Han, Stanley Osher, Wuchen Li; (85):1−66, 2026.
[abs][pdf][bib]

Best Arm Identification with Minimal Regret: Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin; (86):1−41, 2026.
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On the Relevance of Byzantine Robust Optimization Against Data Poisoning: Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot; (87):1−44, 2026.
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Node Regression on Latent Position Random Graphs via Local Averaging: Martin Gjorgjevski, Nicolas Keriven, Simon Barthelme, Yohann De Castro; (88):1−49, 2026.
[abs][pdf][bib] [code]

Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions: Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu; (89):1−36, 2026.
[abs][pdf][bib]

Transfer Conformal Predictive Inference for Regression: Ce Zhang, Ting Li, Jinhan Xie, Linglong Kong, Bei Jiang; (90):1−68, 2026.
[abs][pdf][bib]

Generalized Resubstitution for Regression Error Estimation: Diego Marcondes, Ulisses Braga-Neto; (91):1−54, 2026.
[abs][pdf][bib] [code]

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation: Shu Liu, Stanley Osher, Wuchen Li; (92):1−75, 2026.
[abs][pdf][bib] [code]

Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent: Henry Lam, Zitong Wang; (93):1−42, 2026.
[abs][pdf][bib] [code]

Transfer Learning via Regularized Random-effects Linear Discriminant Analysis: Hongzhe Zhang, Arnab Auddy, Hongzhe Li; (94):1−54, 2026.
[abs][pdf][bib]

Semi-supervised learning for linear extremile regression: Rong Jiang, Jiangfeng Wang, Keming Yu; (95):1−24, 2026.
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Deep Nonparametric Conditional Independence Tests for Images: Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven; (96):1−73, 2026.
[abs][pdf][bib] [code]

Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data: Luoyao Yu, Lixing Zhu, Ruoqing Zhu, Xuehu Zhu; (97):1−52, 2026.
[abs][pdf][bib] [code]

Exogenous Randomness Empowering Random Forests: Tianxing Mei, Yingying Fan, Jinchi Lv; (98):1−95, 2026.
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High-dimensional Parameter Transfer With Fused-Regularizer: Zelin He, Ying Sun, Jingyuan Liu, Runze Li; (99):1−54, 2026.
[abs][pdf][bib] [code]

Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines: Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda, Hachem Kadri; (100):1−38, 2026.
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Deconvolution in unlinked linear models: Balabdaoui, Fadoua, Di Noia, Antonio, Durot, Cécile; (101):1−39, 2026.
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Statistical Learning Theory for Neural Operators: Niklas Reinhardt, Sven Wang, Jakob Zech; (102):1−82, 2026.
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A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization: Yongcun Song, Zimeng Wang, Xiaoming Yuan, Hangrui Yue; (103):1−43, 2026.
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Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks: Nikolaos Tsilivis, Eitan Gronich, Julia Kempe, Gal Vardi; (104):1−37, 2026.
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A Unified Approach to Analysis and Design of Denoising Markov Models: Yinuo Ren, Grant M. Rotskoff, Lexing Ying; (105):1−69, 2026.
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Nested Subspace Learning with Flags: Tom Szwagier, Xavier Pennec; (106):1−48, 2026.
[abs][pdf][bib] [code]

FLAGG: Flexible Autoregressive Graph Generation: Samuel Cognolato, Alessandro Sperduti, Luciano Serafini; (107):1−38, 2026.
[abs][pdf][bib] [code]

A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance: Axel F. Wolter, Tobias Sutter; (108):1−68, 2026.
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Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex Optimization: Siyuan Zhang, Nachuan Xiao, Xin Liu; (109):1−52, 2026.
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Three Types of Calibration using Properties and their Semantic and Formal Relationships: Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson; (110):1−52, 2026.
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Embedding Network Autoregression for Time Series Analysis and Causal Peer Effect Inference: Jae Ho Chang, Subhadeep Paul; (111):1−72, 2026.
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Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation: Yuze Han, Xiang Li, Zhihua Zhang; (112):1−69, 2026.
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The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler: Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe, Sifan Liu; (113):1−64, 2026.
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STDE++: Polynomial-Time Amortization for Linear Differential Operators: Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi; (114):1−50, 2026.
[abs][pdf][bib] [code]

Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes: Tim Gyger, Reinhard Furrer, Fabio Sigrist; (115):1−60, 2026.
[abs][pdf][bib] [code]

Statistical guarantees for denoising reflected diffusion models: Asbjørn Holk, Claudia Strauch, Lukas Trottner; (116):1−50, 2026.
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Learning general conditional independence structures via the neighbourhood lattice: Arash A. Amini, Bryon Aragam, Qing Zhou; (117):1−42, 2026.
[abs][pdf][bib] [code]

Accelerating Constrained Sampling: A Large Deviations Approach: Yingli Wang, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu; (118):1−61, 2026.
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Statistical Test for Attention in Transformers for Images and Time Series: Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Shuichi Nishino, Kouichi Taji, Ichiro Takeuchi; (119):1−43, 2026.
[abs][pdf][bib] [code]

py/cuTAGI: An Open-Source Library for Tractable Approximate Gaussian Inference in Bayesian Neural Networks: Luong-Ha Nguyen, James-A. Goulet, Miquel Florensa-Montilla, Van-Dai Vuong; (120):1−8, 2026.
[abs][pdf][bib] [code]

Gradient Span Algorithms Make Predictable Progress in High Dimension: Felix Benning, Leif Döring; (121):1−62, 2026.
[abs][pdf][bib] [code]

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss: José Manuel de Frutos, Manuel A. Vázquez, Pablo M. Olmos, Joaquín Míguez; (122):1−49, 2026.
[abs][pdf][bib] [code]

Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes: Bohan Wu, Eli N. Weinstein, Sohrab Salehi, Yixin Wang, David M. Blei; (123):1−63, 2026.
[abs][pdf][bib] [code]

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes: Ruiqi Zhang, Jingfeng Wu, Licong Lin, Peter L. Bartlett; (124):1−31, 2026.
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Approximation-Free Differentiable Oblique Decision Trees: Subrat Prasad Panda, Blaise Genest, Arvind Easwaran; (125):1−31, 2026.
[abs][pdf][bib] [code]

Underdamped Langevin MCMC with third order convergence: Maximilian Scott, Dáire O'Kane, Andraž Jelinčič, James Foster; (126):1−63, 2026.
[abs][pdf][bib] [code]

Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression: Filippo Ascolani, Giacomo Zanella; (127):1−42, 2026.
[abs][pdf][bib] [code]

Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy: Philip Sosnin, Matthew Wicker, Josh Collyer, Calvin Tsay; (128):1−58, 2026.
[abs][pdf][bib] [code]

Doubly Debiased Robust Subsampling for Transfer Learning: Tao Wang, Weng Kee Wong; (129):1−53, 2026.
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Learning to Play Two-Player Perfect-Information Games without Knowledge: Quentin Cohen-Solal; (130):1−64, 2026.
[abs][pdf][bib]

Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspective: Wenlong Lyu, Yuheng Jia, Hui Liu, Junhui Hou; (131):1−44, 2026.
[abs][pdf][bib] [code]

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs: Yidong Zhou, Su I Iao, Hans-Georg Müller; (132):1−38, 2026.
[abs][pdf][bib] [code]

The Sample Complexity of Parameter-Free Stochastic Convex Optimization: Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder; (133):1−46, 2026.
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Near-optimal Delta-convex Estimation of Lipschitz Functions: Gábor Balázs; (134):1−41, 2026.
[abs][pdf][bib] [code]

Error Analyses of Auto-Regressive Video Diffusion Models: Jing Wang, Fengzhuo Zhang, Xiaoli Li, Vincent Y.~ F. Tan, Tianyu Pang, Chao Du, Aixin Sun, Zhuoran Yang; (135):1−51, 2026.
[abs][pdf][bib]

High-Dimensional Analysis of Gradient Flow for Extensive-Width Quadratic Neural Networks: Simon Martin, Giulio Biroli, Francis Bach; (136):1−182, 2026.
[abs][pdf][bib] [code]

Bridging Domain Invariance and Diversity: A Fine-Grained Risk Bound for Domain Generalization: Xi Wang, Liang Bai, Xian Yang, Richard Yi Da Xu, Jiye Liang; (137):1−54, 2026.
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The Role of Pseudo-Labels in Self-Training Linear Classifiers on High-Dimensional Gaussian Mixture Data: Takashi Takahashi; (138):1−68, 2026.
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Keypoint-Guided Optimal Transport: Models, Algorithms, and Applications: Xiang Gu, Yucheng Yang, Wei Zeng, Jian Sun, Zongben Xu; (139):1−60, 2026.
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Adversarial Rademacher Complexity of Deep Neural Networks: Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Zhi-Quan Luo; (140):1−46, 2026.
[abs][pdf][bib] [code]

Model-free generalized fiducial inference: Jonathan P Williams; (141):1−28, 2026.
[abs][pdf][bib]

torchgfn: A PyTorch GFlowNet Library: Joseph D. Viviano, Omar G. Younis, Sanghyeok Choi, Victor Schmidt, Yoshua Bengio, Salem Lahlou; (142):1−15, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Integrative Regression: Yue Liu, Molei Liu, Zijian Guo, Tianxi Cai; (143):1−53, 2026.
[abs][pdf][bib] [code]

Dirichlet Active Learning: Kevin Miller, Ryan Murray; (144):1−63, 2026.
[abs][pdf][bib] [code]

Information-Theoretic Safe Bayesian Optimization: Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters; (145):1−42, 2026.
[abs][pdf][bib]

Singular-limit analysis of gradient descent with noise injection: Anna Shalova, André Schlichting, Mark Peletier; (146):1−56, 2026.
[abs][pdf][bib]

Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method: Bryan Andrews, Erich Kummerfeld; (147):1−42, 2026.
[abs][pdf][bib] [code]

Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models: Dapeng Shi, Tiandong Wang, Zhiliang Ying; (148):1−63, 2026.
[abs][pdf][bib]

Online Generalized Sparse Regression: How Does Overparametrization Help?: Shuoguang Yang, Qiang Sun; (149):1−48, 2026.
[abs][pdf][bib]

Extrapolation-Aware Nonparametric Statistical Inference: Niklas Pfister, Peter Bühlmann; (150):1−59, 2026.
[abs][pdf][bib] [code]

Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting: Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli; (151):1−46, 2026.
[abs][pdf][bib]

Causal Falsification of Digital Twins: Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet, Chris Holmes; (152):1−52, 2026.
[abs][pdf][bib] [code]

Sliced Wasserstein Regression: Han Chen, Yidong Zhou, Hans-Georg Müller; (153):1−69, 2026.
[abs][pdf][bib] [code]

Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models: Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie; (154):1−60, 2026.
[abs][pdf][bib] [code]

Conditional Regression for the Nonlinear Single-Variable Model: Yantao Wu, Mauro Maggioni; (155):1−75, 2026.
[abs][pdf][bib] [code]

Impatient Bandits: Optimizing for the Long-Term Without Delay: Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek, Lucas Maystre, Daniel Russo; (156):1−58, 2026.
[abs][pdf][bib]

A Neural Network Approach to Learning Solutions of a Class of Elliptic Variational Inequalities: Amal Alphonse, Michael Hintermüller, Alexander Kister, Chin Hang Lun, Clemens Sirotenko; (157):1−48, 2026.
[abs][pdf][bib] [code]

Differentially Private Synthetic Data Generation for Relational Databases: Kaveh Alim, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan; (158):1−50, 2026.
[abs][pdf][bib] [code]

Bayesian Level Set Clustering: David Buch, Miheer Dewaskar, David B. Dunson; (159):1−68, 2026.
[abs][pdf][bib] [code]

Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions: Claire Donnat, Elena Tuzhilina; (160):1−84, 2026.
[abs][pdf][bib] [code]

Pairwise Comparisons without Stochastic Transitivity: Model, Theory and Applications: Sze Ming Lee, Yunxiao Chen; (161):1−49, 2026.
[abs][pdf][bib] [code]

Have ASkotch: A Neat Solution for Large-Scale Kernel Ridge Regression: Pratik Rathore, Zachary Frangella, Jiaming Yang, Michał Dereziński, Madeleine Udell; (162):1−48, 2026.
[abs][pdf][bib] [code]

Optimal Convergence Rates for Neural Operators: Mike Nguyen, Nicole Mücke; (163):1−78, 2026.
[abs][pdf][bib]

Deep Neural Expected Shortfall Regression with Tail-Robustness: Myeonghun Yu, Kean Ming Tan, Huixia Judy Wang, Wen-Xin Zhou; (164):1−98, 2026.
[abs][pdf][bib]

Identifiability of the Instrumental Variable Model with the Treatment and Outcome Missing Not at Random: Shuozhi Zuo, Peng Ding, Fan Yang; (165):1−65, 2026.
[abs][pdf][bib] [code]

Statistical Inference for High-dimensional Partially Linear Models via Debiased Rank Lasso: Songshan Yang, Delin Zhao, Runze Li; (166):1−75, 2026.
[abs][pdf][bib]

Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters: Sebastian Salwig, Till Kahlke, Florian Hirschberger, Dennis Forster, Jörg Lücke; (167):1−70, 2026.
[abs][pdf][bib] [code]

Nonparametric Spectral Density Estimation using Interactive Mechanisms under Local Differential Privacy: Cristina Butucea, Karolina Klockmann, Tatyana Krivobokova; (168):1−49, 2026.
[abs][pdf][bib] [code]

Test-time regression: a unifying framework for designing sequence models with associative memory: Ke Alexander Wang, Jiaxin Shi, Emily B. Fox; (169):1−41, 2026.
[abs][pdf][bib]

Particle Filter for Bayesian Inference on Privatized Data: Yu-Wei Chen, Pranav Sanghi, Jordan Awan; (170):1−44, 2026.
[abs][pdf][bib] [code]

A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization: Tianshu Chu, Dachuan Xu, Wei Yao, Chengming Yu, Jin Zhang; (171):1−41, 2026.
[abs][pdf][bib]

Incorporating external data for analyzing randomized clinical trials: A transfer learning approach: Yujia Gu, Hanzhong Liu, Wei Ma; (172):1−61, 2026.
[abs][pdf][bib]

Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning: Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su; (173):1−73, 2026.
[abs][pdf][bib]

Leakage and Interpretability in Concept-Based Models: Enrico Parisini, Tapabrata Chakraborti, Chris Harbron, Ben D. MacArthur, Christopher R.S. Banerji; (174):1−40, 2026.
[abs][pdf][bib] [code]

Clustering and Pruning in Causal Data Fusion: Otto Tabell, Santtu Tikka, Juha Karvanen; (175):1−56, 2026.
[abs][pdf][bib] [code]

Viscosity Convergence Analysis for Deep Q-Networks: Qian Qi; (176):1−26, 2026.
[abs][pdf][bib]

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks: Zihan Shao, Konstantin Pieper, Xiaochuan Tian; (177):1−58, 2026.
[abs][pdf][bib] [code]

Consistency of Augmentation Graph and Network Approximability in Contrastive Learning: Chenghui Li, A. Martina Neuman; (178):1−68, 2026.
[abs][pdf][bib] [code]

Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions: Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu; (179):1−50, 2026.
[abs][pdf][bib] [code]

On the Effectiveness of the z-Transform Method in Quadratic Optimization: Francis Bach; (180):1−43, 2026.
[abs][pdf][bib]

Domain Adaptation Targeting Heterogeneous and Imbalanced Subgroups: Doudou Zhou, Mengyan Li, Yun Wang, Tianxi Cai, Molei Liu; (181):1−65, 2026.
[abs][pdf][bib]

Dimension Reduction for Derivative-Informed Operator Learning: An Analysis of Approximation Errors: Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas; (182):1−94, 2026.
[abs][pdf][bib]

Locally Private Estimation with Public Features: Yuheng Ma, Hanfang Yang, Ke Jia; (183):1−58, 2026.
[abs][pdf][bib]

scikit-activeml: A Comprehensive and User-Friendly Active Learning Library: Marek Herde, Minh Tuan Pham, Daniel Kottke, Alexander Benz, Lukas Lührs, Pascal Mergard, Christoph Sandrock, Jiaying Cheng, Atal Roghman, Mehmet Müjde, Lukas Rauch, Bernhard Sick; (184):1−20, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Ehrenfeucht-Haussler Rank and Chain of Thought: Pablo Barceló, Alexander Kozachinskiy, Tomasz Steifer; (185):1−41, 2026.
[abs][pdf][bib]

Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection: Lin Lu, Yuyang Huo, Haojie Ren, Zhaojun Wang, Changliang Zou; (186):1−86, 2026.
[abs][pdf][bib]

AgentPEN: A Prediction-Explanation Network for Sequential Stock Movement via LLMs and Recurrent Generation: Shuqi Li, Mengyao Guo, Yunzhong Zheng, Siqi Li, Xin Gao, Rui Yan; (187):1−37, 2026.
[abs][pdf][bib] [code]

Safe Learning Under Irreversible Dynamics via Asking for Help: Benjamin Plaut, Juan Liévano-Karim, Hanlin Zhu, Stuart Russell; (188):1−43, 2026.
[abs][pdf][bib]

Pointwise Confidence Estimation in the Non-linear $\ell^2$-regularized Least Squares: Ilja Kuzborskij, Yasin Abbasi Yadkori; (189):1−43, 2026.
[abs][pdf][bib]

torchsom: The Reference PyTorch Library for Self-Organizing Maps: Louis Berthier, Ahmed Shokry, Maxime Moreaud, Guillaume Ramelet, Eric Moulines; (190):1−17, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

Gradient Estimation for Mixture Variational Inference: Javier Burroni, Daniel Sheldon; (191):1−34, 2026.
[abs][pdf][bib]

Adaptive Algorithms for Infinitely Many-Armed Bandits: A Unified Framework: Emmanuel Pilliat; (192):1−25, 2026.
[abs][pdf][bib] [code]

Efficient Inference under Label Shift in Unsupervised Domain Adaptation: Seong-ho Lee, Yanyuan Ma, Jiwei Zhao; (193):1−50, 2026.
[abs][pdf][bib]

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis: Łukasz Dębowski; (194):1−35, 2026.
[abs][pdf][bib]

Symmetric Rank-k Methods: Chengchang Liu, Cheng chen, Luo Luo; (195):1−33, 2026.
[abs][pdf][bib]

Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach: Luca Presicce, Sudipto Banerjee; (196):1−60, 2026.
[abs][pdf][bib] [code]

Optimising Utility Functions in Multi-Objective Markov Decision Processes: Manel Rodriguez-Soto; (197):1−53, 2026.
[abs][pdf][bib]

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning: Zijian Guo, Mengchu Zheng, Peter Bühlmann; (198):1−67, 2026.
[abs][pdf][bib] [code]

A Theoretical Framework for Masked Pretraining (MPT): Qi Zhang, Runyu Zhou, Yifei Wang, Yisen Wang; (199):1−46, 2026.
[abs][pdf][bib]

From learnable objects to learnable random objects: Aaron Anderson, Michael Benedikt; (200):1−55, 2026.
[abs][pdf][bib]

Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations: Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino; (201):1−63, 2026.
[abs][pdf][bib] [code]

Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods: Xinyang Hu, Fengzhuo Zhang, Siyu Chen, Zhuoran Yang; (202):1−104, 2026.
[abs][pdf][bib]

OptunaHub: A Platform for Black-Box Optimization: Yoshihiko Ozaki, Shuhei Watanabe, Toshihiko Yanase; (203):1−10, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models: Leyi Pan, Sheng Guan, Zheyu Fu, Luyang Si, Huan Wang, Zian Wang, Hanqian Li, Xuming Hu, Irwin King, Philip S. Yu, Aiwei Liu, Lijie Wen; (204):1−22, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

A Library for Learning Neural Operators: Jean Kossaifi, Nikola Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert J. George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Valentin Duruisseaux, Anima Anandkumar; (205):1−6, 2026. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]

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