stat.ML · 2026-09-29 · No. 128

Machine Learning, 2026-09-29.

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

    Elicitation and Decision Geometry in Single-Index Bandits

    Sakshi Arya, Cheng Soon Ong

    stat.ML · cs.LG

    We study two-arm contextual bandits with arm-specific single indices and a shared unknown monotone link. Monotonicity makes the optimal action depend only on the contrast between the index directions, hence arm-specific reward functions need not be estimated. We introduce Natural Boundary Learning (NBL), a greedy procedure that uses a sequential Stein contrast to learn the optimal boundary directly, without estimating the reward functions or...

    arxiv.org/abs/2609.35622 · PDF

  2. 02

    Learning Conditional Expectation Operators via Functional Newton Updates

    Thiago Ramos, Alek Fröhlich, Daniel Perazzo, Massimiliano Pontil

    stat.ML · cs.LG

    We introduce the Functional Spectral-Newton Method (FSNM) for learning the leading singular structure of a conditional expectation operator without fixing a basis or reproducing kernel Hilbert space. FSNM fits a low-rank representation of the centered joint-to-product density ratio kernel by alternating functional Newton updates. Each update reduces to a preconditioned regression, which we approximate with vector-valued regression trees in a...

    arxiv.org/abs/2609.35598 · PDF

  3. 03

    Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification

    Sami Chemlal, Thibaut Germain, Rémi Flamary, Vladimir R. Kostic, Karim Lounici

    stat.ML · cs.LG

    Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven applications, including uncertainty quantification and dynamical systems analysis. Conditional mean operators (CMOs), a class of linear operators between function spaces, resolve these objectives by providing access to a broad class of conditional statistics. However, existing methods typically...

    arxiv.org/abs/2609.35429 · PDF

  4. 04

    A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty

    Niklas Koenen, Claudia Battistin, Jeriek Van den Abeele, Martin Jullum

    stat.ML · cs.LG

    Modern probabilistic machine learning models increasingly produce multivariate outputs with complex dependence structure, from multi-step time-series forecasts to sample path predictions. Understanding which input features drive the predictive uncertainty is important for risk-aware decisions, model diagnostics, and deciding whether the uncertainty should be mitigated or hedged against. This attribution problem requires a choice of how...

    arxiv.org/abs/2609.35217 · PDF

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