stat.ML · 2026-08-20 · No. 90

Machine Learning, 2026-08-20.

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

01 — The papers

4 entries
  1. 01

    Learning Random Geometric Graphs Drawn in Probabilistic Metric Spaces

    Dalia Chakrabarty, Kangrui Wang, Chuqiao Zhang, Ye Liu

    stat.ML · cs.LG · stat.AP

    We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric space. This graph learning works for generic datasets, irrespective of the type of the observables; their probability distributions; or size of the data. We identify a metric of the space that the graph is drawn in, as a probability distribution of a random variable that we introduce, namely, a...

    arxiv.org/abs/2608.19082 · PDF

  2. 02

    Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification

    Yuga Iguchi, Paul Fearnhead

    stat.ML · cs.LG · math.ST

    The empirical success of diffusion models in generative modelling has motivated theoretical work, including quantitative error bounds and qualitative analyses that characterise the different phases of denoising. We bring these two areas together by studying the adaptivity of diffusion models to the structured geometry of multimodal high-dimensional data that consists of multiple clusters in $\mathbb{R}^D$, each with its own low-dimensional...

    arxiv.org/abs/2608.19067 · PDF

  3. 03

    Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration

    Matthias Mandl, Hanne Kekkonen

    stat.ML · cs.LG

    We study Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model. Existing GP-UCB analyses in this setting typically require the exploration parameter to grow with the horizon to maintain uniform confidence bounds. Using per-round local confidence events, we show that GP-UCB can instead be run with a constant exploration parameter and obtain an expected-regret...

    arxiv.org/abs/2608.18863 · PDF

  4. 04

    Inference and Uncertainty Quantification for Streaming $r$-PCA

    Haoshu Xu, Hongzhe Li

    stat.ML · cs.LG

    We address two open questions in streaming PCA via Oja's algorithm: sharp operator-norm convergence for general rank under sub-Gaussian data, and distributional inference for the resulting subspace estimator. Existing convergence analyses, even in the rank-one case, either assume bounded data or leave non-vanishing remainder terms that prevent adaptation to a polynomially vanishing tail spectrum, while existing distributional results are...

    arxiv.org/abs/2608.18374 · PDF

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