stat.ML · 2026-07-01 · No. 40

Machine Learning, 2026-07-01.

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

    Accelerating Conformal Prediction via Approximate Leave-One-Out

    Jiachen Cong, Jingbo Liu

    stat.ML · cs.LG

    While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost. Recent methods, including Jackknife+ and Jackknife-minmax, achieve faster computation by trading a slight loss of efficiency relative to full conformal prediction, but still requires computing leave-one-out refits for all observations. In this paper, we further accelerate...

    arxiv.org/abs/2606.31915 · PDF

  2. 02

    MNAR-$k$-means: A $k$-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability

    Xin Guan

    stat.ML · cs.LG

    The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values. A natural extension of $k$-means to missing data is to involve only the observed positions in clustering, which is equivalent to imputing missing values by corresponding cluster means. However, for data missing not at random (MNAR), since missingness is related to data values, such a...

    arxiv.org/abs/2606.31253 · PDF

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