stat.ML · 2026-06-12 · No. 21

Machine Learning, 2026-06-12.

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

01 — The papers

4 entries
  1. 01

    Majority-of-Three is Optimal

    Divit Rawal, Nikita Zhivotovskiy

    stat.ML · cs.LG · math.ST

    We give a short proof that the majority vote of three independent consistent classifiers is an optimal learner in the realizable PAC setting. This proves optimality for the simplest voting scheme, while simplifying both the algorithmic structure and the probabilistic analysis of previous voting learners, including the algorithm of S. Hanneke and the analysis of bagging by K. Green Larsen.

    arxiv.org/abs/2606.13614 · PDF

  2. 02

    Simultaneous Latent Budget Trees for Stratified Classification

    Simultaneous Latent Budget Trees for Stratified Classification Cristian Buoncompagni, Stefano Pellegrino, Giulia...

    stat.ML · cs.LG · stat.ME

    In the era of Explainable Artificial Intelligence, there is a renewed focus on single trees for their ease of interpretation. This paper introduces Simultaneous Latent Budget Trees, a probabilistic machine learning framework for classification trees in the presence of a stratification factor such as a temporal, spatial, or demographic variable, acting as a control variable or potential confounder. Standard tree growth procedures are not...

    arxiv.org/abs/2606.13295 · PDF

  3. 03

    ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization

    Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen

    stat.ML · cs.LG

    Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations to normal training samples, training a classifier to recognize transformation-specific patterns that help identify anomalies through increased classification errors. Despite their strong performance, a significant challenge is their lack of explainability, as they...

    arxiv.org/abs/2606.13277 · PDF

  4. 04

    Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering

    Federico P. Cortese, Alessio Farcomeni

    stat.ML · cs.LG · stat.ME

    We propose a robust feature-weighted jump model for time-dependent clustering. A penalty is used to encourage smoothness of transitions over time, while robustness is achieved through the use of a Tukey's biweight loss function. An additional parameter controls the variability of feature weights across states, allowing the model to assign state-specific relevance to each feature. We illustrate in simulation how the method accurately recovers...

    arxiv.org/abs/2606.13146 · PDF

This edition is part of The Daily Abstract — stat.ML archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.