stat.ML · 2026-08-06 · No. 76

Machine Learning, 2026-08-06.

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

01 — The papers

3 entries
  1. 01

    Stable Density Ridges: Consistency and Convergence of Subspace Constrained Mean Shift

    Wanli Qiao

    stat.ML · cs.LG

    The Subspace Constrained Mean Shift (SCMS) algorithm is a popular nonparametric method for extracting density ridges, which serve as a low-dimensional representation of high-dimensional data. It is a widely held belief in the literature that SCMS trajectories converge to the classical density ridge, which we call the "static ridge", defined via the density gradient and the eigenvalues and eigenvectors of the density's Hessian. In this paper,...

    arxiv.org/abs/2608.05112 · PDF

  2. 02

    Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds

    Yizhu Wang, Mu Niu, Xiaochen Yang

    stat.ML · cs.LG

    We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds. While diffusion models (DMs) have achieved state-of-the-art results in high-dimensional data synthesis, they rely on large training datasets and ignore intrinsic geometric structure. Latent diffusion models (LDMs) address the high dimensionality by...

    arxiv.org/abs/2608.04827 · PDF

  3. 03

    Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection

    Teruyuki Katsuoka, Tomohiro Shiraishi, Shuichi Nishino, Ichiro Takeuchi

    stat.ML · cs.LG

    Selective inference (SI) provides statistically valid $p$-values for hypotheses selected by applying an algorithm to the data, correcting for the bias that arises when the same data are used both to select and to test a hypothesis. Developing an SI procedure for a new algorithm, however, has required an expert to derive, and then implement, the selection event, i.e., the conditions under which the hypothesis is selected. Repeating this...

    arxiv.org/abs/2608.04667 · PDF

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