Bridging Journals and the NeurIPS Community: Journal Track at NeurIPS 2025
The NeurIPS Journal Track aims to strengthen the bridge between machine learning and statistics communities and enable the dissemination of scientific knowledge. Journals such as JMLR and AoS accept rigorously reviewed papers on a rolling schedule outside of the conference cycle. This track allows the top accepted papers to also be presented at NeurIPS to get increased visibility and spark discussion.
This year, we are thrilled to feature 34 accepted papers — 14 from the Journal of Machine Learning Research (JMLR) and 20 from the Annals of Statistics (AoS) — all of which will be presented as posters at NeurIPS 2025 in San Diego. These papers represent the highest standards of scholarship across theory, methodology, and applications, and they exemplify how journal-based research continues to enrich the broader NeurIPS community.
We invite all attendees to visit the Journal Track posters, meet the authors, and engage in discussions that highlight the evolving connections between machine learning and statistics.
Accepted Papers from the Journal of Machine Learning Research (JMLR)
1) RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
https://www.jmlr.org/papers/v25/24-0248.html
2) Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds
Hao Liang, Zhi-Quan Luo
https://www.jmlr.org/papers/v25/22-1253.html
3) Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
Fanghui Liu, Leello Dadi, Volkan Cevher
https://jmlr.org/papers/v25/22-1250.html
4) Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli
https://www.jmlr.org/papers/v25/24-0605.html
5) Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy
https://jmlr.org/papers/v25/22-1434.html
6) Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy
https://jmlr.org/papers/v25/23-0115.html
7) Dropout Regularization Versus l2-Penalization in the Linear Model
Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber
https://www.jmlr.org/papers/v25/23-0803.html
8) Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data
Thomas Chen, Patrícia Muñoz Ewald
https://jmlr.org/papers/v26/24-1516.html
9) Stochastic-Constrained Stochastic Optimization with Markovian Data
Yeongjong Kim, Dabeen Lee
https://jmlr.org/papers/v25/23-1630.html
10) Geometric Learning with Positively Decomposable Kernels
Nathaël Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega, and Salem Said
https://jmlr.org/papers/v25/23-1400.html
11) The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang
https://www.jmlr.org/papers/volume26/24-0100/24-0100.pdf
12) On the Convergence of Projected Policy Gradient for Any Constant Step Sizes
Jiacai Liu, Wenye Li, Dachao Lin, Ke Wei, Zhihua Zhang
https://www.jmlr.org/papers/volume26/24-1530/24-1530.pdf
13) Data-Driven Performance Guarantees for Classical and Learned Optimizers
Rajiv Sambharya, Bartolomeo Stellato
https://jmlr.org/papers/volume26/24-0755/24-0755.pdf
14) Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
Michal Dereziński, Daniel LeJeune, Deanna Needell, Elizaveta Rebrova
https://jmlr.org/papers/volume26/24-1906/24-1906.pdf
Accepted Papers from the Annals of Statistics (AoS)
1) Versatile Differentially Private Learning for General Loss Functions
Song X Chen, Qilong Lu, Yumou Qiu
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/71306?confirm=2c6478db
2) Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Zhenyu Wang, Peter Bühlmann, Zijian Guo
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/69288?confirm=dbfbb000
3) Neural Networks Generalize on Low Complexity Data
Sourav Chatterjee and Timothy Sudijono
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/67274?confirm=7d909619
4) Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
Kaizheng Wang
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/59591?confirm=539ffe2f
5) A Geometrical Analysis of Kernel Ridge Regression and its Applications
Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/66957?confirm=79d1f75f
6) Improved Learning Theory for Kernel Distribution Regression with Two-Stage Sampling
François Bachoc, Louis Béthune, Alberto González-Sanz, Jean-Michel Loubes
7) Robust Transfer Learning with Unreliable Source Data
Jianqing Fan, Cheng Gao, Jason M. Klusowski
8) Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
Weidong Liu, Jiyuan Tu, Yichen Zhang, Xi Chen
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/66491?confirm=9350ae31
9) Estimation and Inference in Distributional Reinforcement Learning
Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang, Zhihua Zhang
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/66608?confirm=25fa0a06
10) Online Statistical Inference in Decision Making with Matrix Context
Qiyu Han, Will Wei Sun, Yichen Zhan
https://www.e-publications.org/ims/submission/AOS/user/submissionFile/64262?confirm=82a6f9e7
11) Asymptotic Theory of Geometric and Adaptive $k$-Means Clustering
Adam Quinn Jaffe
12) Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data
Rui Miao, Babak Shahbaba, Annie Qu
13) Policy Larning “without” overlap: Pessimism and Generalized Empirical Bernstein’s Inequality
Ying Jin, Zhimei Ren, Zhuoran Yang, Zhaoran Wang
14) Testing Stationarity and Change Point Detection in Reinforcement Learning
Mengbing Li, Chengchun Shi, Zhenke Wu, Piotr Fryzlewicz
15) A Duality Framework for Analyzing Random Feature and Two-Layer Neural Networks
Hongrui Chen, Jihao Long, Lei Wu
16) Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
Fan Chen, Song Mei, Yu Bai
17) Multivariate Dynamic Mediation Analysis under a Reinforcement Learning Framework
Lan Luo, Chengchun Shi, Jitao Wang, Zhenke Wu, Lexin Li
18) A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules
Xiang Li, Feng Ruan, Huiyuan Wang, Qi Long, Weijie J. Su
19) Statistical Inference for Decentralized Federated Learning
Jia Gu, Song Xi Chen
20) Deep Nonlinear Sufficient Dimension Reduction
YinFeng Chen, YuLing Jiao, Rui Qiu, Zhou Yu
These presentations highlight the growing collaboration between NeurIPS and the leading journals that define our field. We look forward to welcoming all authors and attendees to San Diego this December for an inspiring exchange of ideas that bridges deep theory, rigorous methods, and transformative applications.
NeurIPS 2025 Journal Chair
Lam Nguyen