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]

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]

[Statistics (Machine Learning) Papers @StatsPapers]

Why Constrained Updates Still Forget: A Functional-Path Analysis of Continual SFT

Qingsen Ma, Dianyun Wang, Jiahong Liu, Irwin King
https://t.co/xiiZjg20p9 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

The Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits

Sharan Sahu, Martin T. Wells
https://t.co/HPfGFI8aZb [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Silent Alignment in Two-Layer Linear Networks: A High-Dimensional Perspective

Simon Martin, Francis Bach, Giulio Biroli
https://t.co/HMy58ca6dO [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Lifting PCA in the Wasserstein Space

Jérémie Bigot, Clément Bonet
https://t.co/VdFQceuii4 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Statistics (Machine Learning) Papers @StatsPapers]

Near Minimax Rate for Quantile Distributional Reinforcement Learning

Zijie Cheng, Zhihua Zhang
https://t.co/2X2RuYjMEw [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression

Daniel Klippert, Quang Nhat Huynh, Alexander Marx
https://t.co/9K01Fdi2wM [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

A unified approach for estimation of unnormalized models via Stein operators

Mitsuki Nagai, Keisuke Yano
https://t.co/38zOF7HHdF [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝚂𝚃]

[Statistics (Machine Learning) Papers @StatsPapers]

When Thoughts Become Memory: Unifying In-Context Learning and Fine-Tuning

Leon Chlon, Julie Huang
https://t.co/soeZ4YRKcV [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]