stat.ML · 2026-06-28 · No. 37

Machine Learning, 2026-06-28.

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

    When are likely answers right? On Sequence Probability and Correctness in LLMs

    Johannes Zenn, Jonas Geiping

    stat.ML · cs.LG

    Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, either locally at the token level or globally at the sequence level. Therefore, their success depends on a fundamental question: when does sequence probability, that is, the conditional probability of a continuation given a prompt, actually align with correctness? In this paper, we set out to...

    arxiv.org/abs/2606.27359 · PDF

  2. 02

    Ribbon: Scalable Approximation and Robust Uncertainty Quantification

    Graham Gibson, John Tipton, Kellin Rumsey, Natalie Klein

    stat.ML · cs.LG

    Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models. Both fully Bayesian and bootstrap resampling methods provide principled uncertainty estimates but are often too expensive for modern machine-learning models because they require posterior sampling or repeated model refitting. We introduce Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty. Ribbon replaces...

    arxiv.org/abs/2606.27269 · PDF

  3. 03

    Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference

    Hanli Xu, Fengxiang He, Sarat Moka

    stat.ML · cs.AI · cs.LG

    Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies their local-mass behaviour that is not directly captured by such objectives. We introduce and use two mathematical tools: (1) Mass Index for recording the polynomial and logarithmic decay scales of local mass, and (2) regularised extended KL (RE-KL), a set-localised divergence that can be...

    arxiv.org/abs/2606.27090 · PDF

  4. 04

    XMSE-Aware Adaptive Empirical Bayes Estimation

    Minghao Chen, Jiale Zheng

    stat.ML · cs.AI · cs.LG · eess.SY · stat.ME

    Empirical Bayes (EB) estimators can match the first-order asymptotic risk of maximum likelihood (ML) while behaving very differently at second order: recent excess mean squared error (XMSE) analysis shows that kernel-based EB estimation may be worse than ML when the kernel is poorly aligned with the true parameter. This paper turns that diagnostic into a design principle. We propose an XMSE-aware mixed estimator that interpolates between ML...

    arxiv.org/abs/2606.26975 · PDF

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