stat.ML · 2026-09-30 · No. 129

Machine Learning, 2026-09-30.

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

    ReCIRC: Rectified Conformal Risk Control

    Bruno Marcondes e Resende, Helton Graziadei, Thiago Rodrigo Ramos, Rafael Izbicki

    stat.ML · cs.LG

    Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or missed labels in multilabel classification. Conformal risk control (CRC; Angelopoulos et al., arXiv:2208.02814) gives distribution-free guarantees for such losses, but it calibrates a single threshold shared by all inputs. Because conditional risk varies with the input, this marginal guarantee often...

    arxiv.org/abs/2609.38112 · PDF

  2. 02

    Latent Inference-Time Guidance of Time Series Foundation Models

    Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi, Benjamin Guedj, Yannig Goude, Sylvain Le Corff

    stat.ML · cs.LG · stat.ME

    Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on in-context learning to make their predictions, which makes the quality of their performance highly sensitive to the user-selected lookback, covariates, horizon and training data distributions. In practise, the quality of the forecasts are variable but complementary, which highlights the need for...

    arxiv.org/abs/2609.38058 · PDF

  3. 03

    Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems

    Julien Boussard, Antoine Débouchage, Théo Saulus

    stat.ML · cs.LG · math.DS

    A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a set of precomputed operations, and causal discovery, which is more principled but usually relies on strong assumptions that physical systems may violate. In this work, we develop a theory-grounded method and prove that under a set of permissive...

    arxiv.org/abs/2609.37944 · PDF

  4. 04

    Post-Anomaly Detection Inference for Deep SVDD

    Cao Le Cong Thanh, Dang Quang Vinh, Vo Nguyen Le Duy

    stat.ML · cs.LG

    Deep Support Vector Data Description (Deep SVDD) has become a prominent framework for unsupervised anomaly detection by learning latent representations that compactly characterize normal data around a center. Despite its empirical success, anomaly decisions produced by Deep SVDD are typically made solely based on anomaly scores without rigorous statistical guarantees, thereby limiting their reliability in safety-critical and high-stakes...

    arxiv.org/abs/2609.37935 · PDF

  5. 05

    A Finslerian Approach for Embedding Directed Data

    Gwendal Debaussart-Joniec, Théau Blanchard, Argyris Kalogeratos

    stat.ML · cs.LG

    Many datasets carry an intrinsic directionality: citations point backward in time, cells differentiate along lineages, and traffic follows preferred routes. Spectral embedding methods, including most of their extensions to directed graphs, discard this information: they symmetrize the data and map it into a Euclidean space where asymmetry cannot be represented. We instead model directed data as sampled from a Finsler manifold, whose distance...

    arxiv.org/abs/2609.37649 · PDF

  6. 06

    Ornstein-Uhlenbeck Is Hard to Beat, Yet Superlinear Drift Ships Lower Transport Costs

    Attila Lovas, Lóránt Nagy

    stat.ML · cs.LG

    Brešar and Mijatović \cite{bresar2025} show that Ornstein--Uhlenbeck diffusion is hard to beat in forward convergence under assumptions that exclude superlinear drift. We instead test superlinear Langevin diffusions for score-based image generation, computing their conditional scores numerically from a Fokker--Planck equation. In our experiments, the superlinear models beat the Ornstein--Uhlenbeck baseline on empirical Wasserstein distance...

    arxiv.org/abs/2609.37579 · PDF

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