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.
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[code]
- Online Detection of Changes in Moment--Based Projections: When to Retrain Deep Learners or Update Portfolios?
- Ansgar Steland; (2):1−50, 2026.
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- 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.
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[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
- Vincent Florian, Waïss Azizian, Franck Iutzeler, Jérôme Malick; (8):1−7, 2026. (Machine Learning Open Source Software Paper)
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[code]
- Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent
- Tong Wu; (9):1−90, 2026.
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[code]
- A Data-Augmented Contrastive Learning Approach to Nonparametric Density Estimation
- Chenghao Li, Yuanyuan Lin; (10):1−47, 2026.
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[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)
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[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.
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[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)
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[code]
- Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification
- Aleksi Avela, Pauliina Ilmonen; (18):1−28, 2026.
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[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.
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[code]
- A Common Interface for Automatic Differentiation
- Guillaume Dalle, Adrian Hill; (25):1−13, 2026. (Machine Learning Open Source Software Paper)
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[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.
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[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.
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[code]
- Hierarchical Causal Models
- Eli N. Weinstein, David M. Blei; (37):1−73, 2026.
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[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.
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[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.
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[code]
- A Symplectic Analysis of Alternating Mirror Descent
- Jonas E. Katona, Xiuyuan Wang, Andre Wibisono; (44):1−61, 2026.
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[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.
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[code]
- Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models
- Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister; (46):1−57, 2026.
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[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.
[abs][pdf][bib]
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