stat.ML · 2026-06-16 · No. 25

Machine Learning, 2026-06-16.

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

01 — The papers

5 entries
  1. 01

    Dynestyx: A Probabilistic Programming Library for Dynamical Systems

    Daniel Waxman, Dmitry Batenkov, John Feser, Andy Zane, Eli Bingham, Youssef Marzouk, Matthew E. Levine

    stat.ML · cs.LG · eess.SP · nlin.CD · stat.ME

    State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to incorporate in modern probabilistic programming languages (PPLs), making state-of-the-art methods less accessible to practitioners and introducing friction in...

    arxiv.org/abs/2606.16985 · PDF

  2. 02

    Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy

    Baicheng Li, Haizhao Yang, Shijun Zhang

    stat.ML · cs.LG

    In this work, we investigate new activation functions for achieving arbitrary-accuracy Sobolev approximation by fixed-size neural networks. We first show that any function in $W^{2,\infty}((a,b)^d)$ can be approximated with arbitrary accuracy, measured in the $W^{1,\infty}$-norm, by a fixed-size neural network using the Elementary Universal Activation Function ($\mathrm{EUAF}$). To extend this result to $W^{s,\infty}((a,b)^d)$ for...

    arxiv.org/abs/2606.16975 · PDF

  3. 03

    A nonparametric two-sample test using a parametric integral probability metric

    Yuha Park, Yongdai Kim

    stat.ML · cs.LG

    Detecting distributional differences between two independent samples is a fundamental problem in statistics and machine learning. Nonparametric two-sample testing provides a principled framework for determining whether two samples are drawn from the same underlying distribution, without assuming any specific parametric form for the distribution. In this study, we propose a new two-sample test statistic based on a newly introduced integral...

    arxiv.org/abs/2606.16941 · PDF

  4. 04

    Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining

    Zhengyuan Gao

    stat.ML · cs.AI · cs.LG

    Causal self-attention is a coupling mechanism: each token's hidden state is updated by a learned mixture of preceding tokens at the same timescale. This paper asks whether a second, temporally slower coupling-a slow sub-system operating on a temporally-downsampled view of the sequence and fed back into the fast path through a zero-initialised gate-complements it. The question is framed in the language of singularly perturbed ordinary...

    arxiv.org/abs/2606.16730 · PDF

  5. 05

    Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

    Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus

    stat.ML · cs.LG

    Diffusion Flow Matching (DFM) has recently emerged as a versatile framework for generative modeling, yet its theoretical convergence properties remain only partially understood. In this work, we provide refined and novel convergence guarantees for Brownian motion based DFMs, focusing on the discretization error. Our analysis is conducted under the Kullback-Leibler (KL) divergence and the 2-Wasserstein distance. Under finite-moment conditions...

    arxiv.org/abs/2606.16610 · 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.