stat.ML · 2026-07-21 · No. 60

Machine Learning, 2026-07-21.

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

01 — The papers

3 entries
  1. 01

    COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering

    Kyungseon Lee, Hankyo Jeong, Kunwoong Kim, Kwanho Lee, Yongdai Kim

    stat.ML · cs.LG

    Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgroups. In such settings, directly extending existing fair clustering algorithms is computationally expensive or numerically unstable, especially when the number of subgroups grows exponentially and some subgroups contain only a few instances. To address these...

    arxiv.org/abs/2607.18119 · PDF

  2. 02

    An Adjoint-Sensitivity Framework for Lost-in-the-Middle Phenomena in Causal Residual Transformers

    Cheng Huan, Hongwei Yuan

    stat.ML · cs.LG · math.OC

    We develop an adjoint-sensitivity framework for positional influence in causal residual Transformers and separate unconditional analytic results from conditional boundary-shape conclusions. The principal unconditional theorem is the residual-to-depth-flow estimate for layer controls converging in $L^1$, complemented by a finite-token-to-Volterra attention estimate that explicitly controls the first cells near the causal endpoint. We define a...

    arxiv.org/abs/2607.17696 · PDF

  3. 03

    An efficient adaptive dimension selection algorithm for multidimensional probit graded response models

    Yu Zhou, Yincai Tang, Bin Lv, Meng Gao

    stat.ML · cs.LG · stat.ME

    Multidimensional graded response models (MGRMs) are widely used for analyzing ordinal questionnaire data in psychological and educational assessments. A central challenge in applying these models is determining the number of latent dimensions. Conventional approaches usually fit multiple fixed-dimensional models and select among them using post-hoc criteria such as AIC, BIC, or cross-validation, which can be computationally demanding and...

    arxiv.org/abs/2607.17654 · PDF

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