stat.ML · 2026-09-25 · No. 124

Machine Learning, 2026-09-25.

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

    Nuclear Norm-Regularized Bayesian Matrix Completion

    Calvin Tolbert

    stat.ML · cs.LG

    Matrix completion, the problem of estimating missing entries in a matrix from noisily observed ones, underlies a diverse array of problems such as recommender systems and counterfactual outcome estimation in panel data. Many algorithms address the problem using regularized least squares, often with the nuclear norm as a regularizer, but this method yields a point estimate with no built-in uncertainty quantification. A Bayesian formulation is...

    arxiv.org/abs/2609.30078 · PDF

  2. 02

    Path-specific harm decomposition: A partial identification framework

    Ruizi Yan, Dennis Frauen, Maresa Schröder, Stefan Feuerriegel

    stat.ML · cs.LG

    A central goal when designing treatment policies is often to "do no harm", that is, to avoid interventions that improve average outcomes while worsening outcomes for some individuals. A widely used notion for harm is the fraction of negatively affected (FNA), defined as the probability that an intervention decreases an individual's outcome. However, in many applications, treatments operate through mediators, and a single "total" FNA can...

    arxiv.org/abs/2609.29938 · PDF

  3. 03

    Robust Detection of LLM-Generated Text under Contamination

    Jiaxun Li, Saptarshi Chakraborty, Ambuj Tewari

    stat.ML · cs.LG

    We study the detection of LLM-generated text under editing and contamination. Modeling human and machine text as finite-order Markov processes with Huber contamination, we characterize an exact boundary for reliable detection under our assumptions. Detection is impossible when contamination is sufficiently large relative to clean-source separation. Below this boundary, a collection of clipped likelihood-ratio tests achieves vanishing...

    arxiv.org/abs/2609.29935 · PDF

  4. 04

    Machine Unlearning for Gibbs Supervised Learning Algorithms

    Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola

    stat.ML · cs.LG

    In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired by empirical risk minimization subject to relative entropy regularization (ERM-RER). Such a method consists of maximizing the expected empirical risk over the dataset to be unlearned subject to a regularization by relative entropy with respect to the original algorithm. The optimization variable...

    arxiv.org/abs/2609.29409 · PDF

  5. 05

    GCUL: Ambiguity Identification in Text Emotion Classification via Cluster-Guided Learning

    Zhongqi Fan, Tianyou Zhang, Fei Chen

    stat.ML · cs.LG

    Selective classification enables a model to abstain from predictions on uncertain instances, but existing approaches typically reject them through confidence scores, predefined coverage constraints or instance-level distance measures. These approaches may overlook the collective geometric structure of difficult samples in learned representation spaces. We propose Guided Clustering-based Uncertain Learning (GCUL), a geometric-guided selective...

    arxiv.org/abs/2609.29327 · PDF

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