stat.ML · 2026-05-30 · No. 12

Machine Learning, 2026-05-30.

6 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion

    Anay Mehrotra, Phuc Tran, Van H. Vu, Manolis Zampetakis

    stat.ML · cs.AI · cs.DS · cs.LG · math.ST

    A central goal of modern causal inference is estimating heterogeneous treatment effects to answer questions like "how does an intervention affect each unit," rather than only on average. We study this problem with panel-data where we observe $n$ units across $m$ times under unknown, non-uniform treatment assignments. The data in this setting is naturally represented as a matrix of all unit--time treatment effects. Estimating heterogeneous...

    arxiv.org/abs/2605.30319 · PDF

  2. 02

    Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series

    Hanyang Jiang, Rina Foygel Barber, Ashwin Pananjady, Yao Xie

    stat.ML · cs.LG · math.ST · stat.ME

    Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable, and predictors are trained in a memoryless fashion. However, these assumptions and constraints are impractical in many real-data settings, such as time series (where temporal dependence violates exchangeability, and where memoryless predictors will inevitably have poor predictive accuracy). Recent work shows...

    arxiv.org/abs/2605.30292 · PDF

  3. 03

    Wasserstein Contraction of Coordinate Ascent Variational Inference

    Rocco Caprio, Adrien Corenflos, Sam Power

    stat.ML · cs.LG · math.FA · math.OC · math.PR · stat.CO

    We study the contraction in Wasserstein distance of the coordinate ascent variational inference algorithm. This is shown to hold under a transport-information inequality at the fixed points and a functional smoothness condition. The results are general and sharp, allow for local convergence guarantees, hold for general smooth manifolds, and also in some non-smooth spaces. We consider applications to Bayesian Gaussian Mixture Models, and...

    arxiv.org/abs/2605.30253 · PDF

  4. 04

    Visual Spatial Learning: Single-Field Spatial Interpolation Using Convolutional Neural Networks

    Daniel Tinoco, Raquel Menezes, Carlos Baquero, Alexandra Silva

    stat.ML · cs.CV · cs.LG · stat.AP

    Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and require substantial domain expertise. In this work, we leverage an architecture based on convolutional neural networks (CNNs)...

    arxiv.org/abs/2605.30167 · PDF

  5. 05

    Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions

    Jingda Wu, Changxiao Cai

    stat.ML · cs.IT · cs.LG · math.ST

    Score-based diffusion models have demonstrated remarkable empirical success in learning high-dimensional distributions, particularly those exhibiting low-dimensional and multi-modal structures. However, theoretical understanding of their statistical efficiency remains limited. Existing theories typically rely on strong regularity assumptions, such as uniformly bounded densities or globally smooth score functions, which fail to capture such...

    arxiv.org/abs/2605.30153 · PDF

  6. 06

    Joint Model and Data Sparsification via the Marginal Likelihood

    Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth, Mohammad Emtiyaz Khan, Eric Nalisnick

    stat.ML · cs.LG

    Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic relevance determination (ARD), offers a practical Bayesian mechanism for feature sparsity via marginal likelihood optimization. Yet, its reliance on a homoscedastic noise model renders it sensitive to data contaminations such as outliers or misspecified noise, harming...

    arxiv.org/abs/2605.29908 · PDF

This edition is part of The Daily Abstract — stat.ML archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.