stat.ML · 2026-09-07 · No. 108

Machine Learning, 2026-09-07.

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

    PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders

    Chloé Hashimoto-Cullen, Ghislain Agoua, Benjamin Guedj, Sylvain Le Corff

    stat.ML · cs.LG

    Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current state of the art models, generative latent variable models are increasingly implemented; yet principled generalisation guarantees for modern latent variable models remain limited. In particular, while Variational AutoEncoders are widely used for sequential data, their theoretical analysis is...

    arxiv.org/abs/2609.05212 · PDF

  2. 02

    FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search

    Cassandra Durr, Alvaro Köhn-Luque, Chris Jewell, Lloyd A. C. Chapman

    stat.ML · cs.LG · physics.data-an

    Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. However, standard methods often produce expressions that violate known physical laws. To address this, we propose FluxDisco, a physics-informed framework tailored for flux-based, stoichiometric ODE systems. By leveraging a known stoichiometry, we reduce the expression search space and ensure...

    arxiv.org/abs/2609.05207 · PDF

  3. 03

    An Analysis of Self-supervised Pre-training with Dependent Samples

    Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe

    stat.ML · cs.LG

    Self-supervised learning relies on so-called data augmentations $φ(x)$ of unlabeled datapoints $x$ --- for example, masking random pixels in an image $x$ --- that should leave the label of $x$ invariant and are often used to learn a lower-complexity invariant subspace $\cal V$ for downstream tasks. In practice, such augmentations $\{ φ_l(x_i) \}$ are pooled together to learn $\cal V$, despite obvious inter-dependencies between different...

    arxiv.org/abs/2609.05031 · PDF

  4. 04

    Minimax Lower Bound for Estimating Diffusion-based Local Intrinsic Dimension

    Jaehee Seo, Wontae Jeong, Jisu Kim

    stat.ML · cs.LG · math.ST

    While diffusion-based methods have recently emerged as effective tools for probing the intrinsic geometry of high-dimensional data, their statistical difficulty remains largely unexplored. We study estimation of the finite-scale population functional underlying FLIPD (Kamkari et al., 2024; arXiv:2406.03537), a diffusion-based local intrinsic dimension (LID) quantity defined through the logarithmic scale derivative of a Gaussian-smoothed...

    arxiv.org/abs/2609.04822 · PDF

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