Statistics (Machine Learning) Papers

New Statistics (Machine Learning) papers from supervised, unsupervised. Thank you to arXiv for use of its open access interoperability.

[Statistics (Machine Learning) Papers @StatsPapers]

Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System

Alban Farchi, Marcin Chrust, Massimo Bonavita, Marc Bocquet, Wei Pan, Patrick Laloyaux
https://t.co/HNXY55yFK7 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Exact Global Minimax Risk near Nonidentification

Kaiyue Yang, Yunan Wu
https://t.co/dafqUcqRnY [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝚂𝚃]

[Statistics (Machine Learning) Papers @StatsPapers]

Empirical concentration for chains with infinite memory

Pierre Alquier, Paul Doukhan
https://t.co/ocjTcjdr39 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Federated Nonparametric Regression with Heterogeneous Clients

Xiaoran Jiang, Anand N. Vidyashankar
https://t.co/Yu26HBiwy7 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Implicit Neural Representations for Variational Problems on Graphons

Taeyoung Kim, Jineon Baek, Joonkyung Lee, Hongseok Yang
https://t.co/tTn3MyEKqL [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝙲𝙾]
💬Accepted at NeurIPS 2026

[Statistics (Machine Learning) Papers @StatsPapers]

Conditional Flow Models for Offline Maximum Entropy Reinforcement Learning

Hao Cheng, Yuling Jiao, Lican Kang, Jerry Zhijian Yang, Chen Zhong
https://t.co/iyQa7F4NOu [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Combinatorial Characterizations of the Learnability of Conditional Distributions

Mihir Dhanakshirur, Ambuj Tewari
https://t.co/bLEqrWRHNT [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Early learning in large residual networks

Vladimir Narovlansky, Boris Hanin
https://t.co/YoLwgb3oQZ [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝙿𝚁 𝚙𝚑𝚢𝚜𝚒𝚌𝚜.𝚍𝚊𝚝𝚊-𝚊𝚗]

[Statistics (Machine Learning) Papers @StatsPapers]

Reliable AI-Assisted Decisions via Selective Auditing

Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani, Tijana Zrnic
https://t.co/126HmhWcqF [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Statistics (Machine Learning) Papers @StatsPapers]

Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting

Rémy Chapelle
https://t.co/Q7HyQqQY6R [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis

Yuansi Chen, Francesco Pedrotti, Peter A. Whalley
https://t.co/yjccnBW5TL [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝙿𝚁 𝚖𝚊𝚝𝚑.𝚂𝚃 𝚜𝚝𝚊𝚝.𝙲𝙾]

[Statistics (Machine Learning) Papers @StatsPapers]

Metric Conformal Prediction via the Conditional Expected Local Radius

Rui Qiu, Fang Yao, Zhou Yu
https://t.co/lNYuPektwj [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions

Marcos Matabuena, Xiaokai Luo, Oscar Hernan Madrid Padilla
https://t.co/Fgxg94Tr9i [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝚂𝚃 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Statistics (Machine Learning) Papers @StatsPapers]

Prediction Uncertainty Controlled Computer Model Calibration

Ryan Heejoon Byun, Dayeon Yoon, Won Chang
https://t.co/p1Dj9nza7O [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚜𝚝𝚊𝚝.𝙰𝙿 𝚜𝚝𝚊𝚝.𝙲𝙾 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Statistics (Machine Learning) Papers @StatsPapers]

Random Projections for Spectral Algorithms: Optimal Rates in Sobolev Norms

Caixing Wang, Yue Wang
https://t.co/SQZ4Vn58qr [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Continuous-depth Deep Gaussian Processes

Ying Li, Sanyou Wu, Zi Yang, Qiaochu Xu, Yuhao Liu, Zhidi Lin, Michael Minyi Zhang, Petar Djuric, Alexandre H. Thiery
https://t.co/HyjSBFFeva [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs

Daniele Micheletti, Federica Zoe Ricci, Erik B. Sudderth
https://t.co/1Ce7UuGci7 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Flow-based Spectral Kernel Learning for Nonstationary Attention

Zi Yang, Ying Li, Alejandro Lancho, Darío C. Larese, Pablo M. Olmos, Michael Minyi Zhang
https://t.co/mRPoHd6PI6 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

High-Value Training Data from Noisy, Diverse Sources

Hedi Driss, Evan Frick, Anastasios N. Angelopoulos, Cho-Jui Hsieh, Yejin Huh, Tijana Zrnic, Andrew Ilyas
https://t.co/UBaIbgtgAj [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Differentially Private Synthetic Data via Generalized Moment Matching

Rundong Ding, Yiyun He, Yizhe Zhu
https://t.co/xPZ6Uy2Gzh [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙸𝚃 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝙿𝚁 𝚖𝚊𝚝𝚑.𝚂𝚃]