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]

Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss

Prashant Shekhar, Caroline Howard
https://t.co/xOZrnqTI8Y [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

A Quantitative Approximation Framework for Flow Distillation in Diffusion Models

Weiguo Gao, Ming Li, Lei Shi, Hanfei Zhou
https://t.co/zWslBhRjUC [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Resource-Constrained Adaptive Inference for Sequential Pricing

Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi
https://t.co/Q0Arx1katb [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Set-Preserving Calibration from Conformal P-Values to E-Values

Nabil Alami, Jad Zakharia, Souhaib Ben Taieb
https://t.co/lcvp9a5MDo [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network

Qingye Tang, Dechao An, Haoran Peng, Yuqi Ouyang
https://t.co/PoLFG4npMZ [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

A Robust Optimization Approach to Sparse Principal Component Analysis

David Vävinggren, Francis Bach, André M. H. Teixeira, Dave Zachariah, Antônio H. Ribeiro
https://t.co/3x8TqqmD4V [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝙾𝙲]

[Statistics (Machine Learning) Papers @StatsPapers]

Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification

Pablo García-Santaclara, Bruno Fernández-Castro, Rebeca Pilar Díaz-Redondo
https://t.co/BUAvWcmBl6 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Hierarchies of Calibration: Classification meets Regression

Johannes Resin, Lu Yang, Tilmann Gneiting
https://t.co/7omLOQl7uG [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

An Asymptotic Theory of Chain-of-Thought in In-Context Learning

Kaito Takanami, Cengiz Pehlevan
https://t.co/YGQugkyFfK [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚘𝚗𝚍-𝚖𝚊𝚝.𝚍𝚒𝚜-𝚗𝚗 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Trajectory-Aware Node Contributions and the Limits of Static Controllability

Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge
https://t.co/CaEycVeemR [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Scalable Derivative Gaussian Processes via Exact Gradient Reduction

Hyunseok Seung, Matthias Katzfuss
https://t.co/cg2uf9PSfB [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

ScoreStop: Gradient-based early stopping using functional score tests

Oliver J. Hines, Christian L. Hines
https://t.co/TOjotrhMEV [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

State-Coupled Volatility in Latent Dynamical Systems: Recovery Under Partial Observation

Imani Beckett
https://t.co/kl0CUG8Osp [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Target Updates May Stabilize Linear Q-Learning: Periodic and Soft Dynamics

Donghwan Lee
https://t.co/QaLXDWwWW9 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery

Tyler H. McCormick
https://t.co/afUHGT4PdM [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝚈 𝚌𝚜.𝙻𝙶 𝚎𝚌𝚘𝚗.𝙴𝙼 𝚜𝚝𝚊𝚝.𝙰𝙿]

[Statistics (Machine Learning) Papers @StatsPapers]

Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

Louise Davy, Stephan Clémençon, Charlotte Laclau
https://t.co/tnp0CzgGC7 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation

David Rundel, Fabian Fumagalli, Maximilian Muschalik, Bernd Bischl, …
https://t.co/ceJKIUM1ta [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]
💬Accepted at the Forty-Third International Conference on Machine Learning (ICML 2026)

[Statistics (Machine Learning) Papers @StatsPapers]

Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families

Roel Hulsman, Carles Balsells-Rodas, Sara Magliacane
https://t.co/uEdiA83epn [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Statistics (Machine Learning) Papers @StatsPapers]

Bayesian meta-learning for modeling Alzheimer's disease progression

Clara Hoffmann, Nadja Klein
https://t.co/GBmTzVe2yN [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙲𝚅 𝚌𝚜.𝙻𝙶]

[Statistics (Machine Learning) Papers @StatsPapers]

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

Tingting Wang, Yunyi Zhang, Benyou Wang
https://t.co/lhpH16S4uY [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚜𝚝𝚊𝚝.𝙼𝙴]