stat.ML · 2026-08-24 · No. 94

Machine Learning, 2026-08-24.

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

01 — The papers

2 entries
  1. 01

    The Exceedance Design Effect: Effective Sample Size for Thresholds under Clustering

    Adam Noonan

    stat.ML · cs.LG

    Many machine-learning systems set a threshold at a quantile of a calibration set: conformal predictors that promise 90% coverage by drawing their cutoff at the calibration set's 90th percentile, abstention gates that decline to answer when a model's score falls below the calibration set's tenth percentile, safety filters that block any output scoring above the 99th percentile of a reference set. All of them promise that the threshold will...

    arxiv.org/abs/2608.21262 · PDF

  2. 02

    Minimax Optimality of Score-Entropy Discrete Diffusion

    Cholyeon Cho, Yuchen Wu

    stat.ML · cs.LG

    Discrete diffusion models have demonstrated strong performance across a range of datasets, including natural language data and graph-structured data. Among many variants, score-entropy discrete diffusion (SEDD) has achieved particularly strong empirical results. In SEDD, new samples are generated by iteratively evaluating a sequence of concrete score functions, which are learned by minimizing a score-entropy loss. While much of the prior...

    arxiv.org/abs/2608.20635 · PDF

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