stat.ML · 2026-08-10 · No. 80

Machine Learning, 2026-08-10.

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

    Optimized Certainty Equivalent Risk Minimization Using Samples: Algorithms, Convergence Rates, and Applications

    Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat

    stat.ML · cs.LG

    We consider the optimization of the Optimized Certainty Equivalent (OCE) risk, with applications including portfolio optimization in finance, and uncertainty quantification, classification, and regression in machine learning. Our contributions cover popular special cases of OCE, such as entropic risk, mean-variance risk, and smooth variants of Conditional Value-at-Risk. Our treatment sets out the conditions that facilitate the extension of...

    arxiv.org/abs/2608.07113 · PDF

  2. 02

    Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces

    Hengchao Chen, Yuanyao Tan, Chao Huang, Hongtu Zhu, Qiang Sun

    stat.ML · cs.LG · math.ST · stat.ME

    This paper introduces Mixtures of Geodesic Factor Analyzers (MGFA) on Riemannian homogeneous spaces. MGFA uses a geodesic factor model within each mixture component, providing greater expressiveness than mixtures of Riemannian radial distributions and enabling clustering of manifold-valued data with anisotropic subpopulations. We establish root-$n$ consistency for the MGFA maximum likelihood estimator (MLE), thereby filling a theoretical gap...

    arxiv.org/abs/2608.06971 · PDF

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