stat.ML · 2026-07-17 · No. 56

Machine Learning, 2026-07-17.

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

    Subjective Risk Decomposition: A New View for Uncertainty Quantification

    Raghad Alamri, Michele Caprio, Gavin Brown

    stat.ML · cs.AI · cs.LG

    We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level modelling decisions. We show how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss. Reverse cross-entropy provides a prominent example, where decomposition recovers the classic...

    arxiv.org/abs/2607.15196 · PDF

  2. 02

    cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

    Chun-houh Chen, Shun-Chuan Chang, Chiun-How Kao, Yi-Ju Lee, Shang-Ying Shiu, Yin-Jing Tien, ShengLi Tzeng, Han-Ming Wu

    stat.ML · cs.LG · stat.CO · stat.ME

    High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables. Existing methods either scale poorly, rely heavily on low-dimensional displays detached from the original data matrix, or prioritize predictive accuracy over interpretability. To address this gap, we introduce categorical Generalized Association Plots...

    arxiv.org/abs/2607.15018 · PDF

  3. 03

    Optimal Self-Distillation for Rectified Flow via Linear Probing

    Saptarshi Roy, Debepsita Mukherjee, Pratik Patil

    stat.ML · cs.LG

    Modern generative models are increasingly trained using model-generated signals, creating both opportunities for self-improvement and risks of collapse. We study optimal self-distillation (SD) for rectified flow (RF): given a suboptimal teacher velocity field, can a student trained on a mixture of true RF velocities and teacher velocities provably improve the teacher? For linear RF with ridge regularization on fixed interpolation pairs, we...

    arxiv.org/abs/2607.14947 · PDF

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