stat.ML · 2026-07-28 · No. 67

Machine Learning, 2026-07-28.

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

01 — The papers

5 entries
  1. 01

    A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection

    Gabriel Singer, Samuel Gruffaz, Olivier Vo Van, Nicolas Vayatis, Argyris Kalogeratos

    stat.ML · cs.LG

    We study imbalanced crowdsourcing with a focus on class-dependent annotator accuracy, a setting that, to the best of our knowledge, remains relatively underexplored despite its importance in real-world inspection systems where the labels of greatest operational importance are also the rarest ones. In this setting, annotators may be reliable on both classes, unreliable on both classes, majority-class specialists, or minority-class specialists....

    arxiv.org/abs/2607.24622 · PDF

  2. 02

    Frequency-Based Reservoir computing

    Arthur S Powanwe

    stat.ML · cs.LG

    Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems. In contrast to other machine and deep learning approaches, a reservoir computing trains only the output layer via linear regression, leaving the reservoir (recurrent layer) untrained. This simplification makes reservoir computers easier to train and more amenable to experimentation. However, because current...

    arxiv.org/abs/2607.24420 · PDF

  3. 03

    proxymate: Diagnosis and Adjustment of Proxy Estimates for Reliable Inference

    Alexandra N. M. Darmon, Deeksha Sinha, Steve Wilkins-Reeves, Caner Gocmen

    stat.ML · cs.LG

    Proxy outcomes (such as short-term behavioral signals, model predictions, or surrogate endpoints) are frequently used in place of primary outcomes that are too slow to mature, rare, or challenging to measure directly. But valid inference on a proxy does not guarantee valid inference on the primary estimate as proxy-based estimates can be systematically biased in ways that are difficult to predict, leading to improperly calibrated confidence...

    arxiv.org/abs/2607.24401 · PDF

  4. 04

    Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD

    Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó

    stat.ML · cs.LG · math.ST

    Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications in two-sample, goodness-of-fit, and independence testing, among others. Their fastest estimators are known to converge at a parametric rate---$n^{-1/2}$---under mild conditions. While this rate is known to be minimax optimal on $\mathbb R^d$ under strict assumptions...

    arxiv.org/abs/2607.24235 · PDF

  5. 05

    On Non-Stationary Dynamic Pricing: Adaptivity and Optimality

    Feiyu Jiang, Zifeng Zhao

    stat.ML · cs.LG

    We study the contextual dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model that can change over time. The demand model is assumed to be a generalized linear model (GLM), allowing for a feature vector in $\mathbb{R}^d$ that encodes products and consumer information. To achieve optimal revenue (i.e., least regret), the firm needs to...

    arxiv.org/abs/2607.24115 · PDF

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