arXiv Machine Learning July 7, 2026 By Samir M. Perlaza, Xinying Zou

The Method of Gaps: Exact Expressions for the Generalization Error of Supervised Learning Algorithms

Read the original on arXiv Machine Learning →

arXiv:2411. 12030v3 Announce Type: replace Abstract: In this paper, the method of gaps, a technique for deriving closed-form expressions in terms of information measures for the generalization error of supervised learning algorithms, is introduced.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

Information-Theoretic Foundations for Machine Learning

arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.

By Hong Jun Jeon, Benjamin Van Roy

arXiv Machine Learning

Jun 15

A General Framework for Decision Trees via Bregman Divergences

arXiv:2606. 13984v1 Announce Type: cross Abstract: Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures.

By Mathias Bourel

safety

arXiv AI

Jul 15

Solution of the Hempel's statistical ambiguity problem and Causal AI

arXiv:2607. 12826v1 Announce Type: new Abstract: This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws.

By Evgenii Vityaev

Hugging Face Trending Papers

Jul 14

Solution of the Hempel's statistical ambiguity problem and Causal AI

This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws. To avoid such predictions, Carl Hempel proposed the Requirement of Maximal Specificity (RMS) for the statistical laws used in the inference.

arXiv AI

Aug 3

A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluation

arXiv:2607. 29077v1 Announce Type: new Abstract: Counterfactual explanations (CEs) enhance the interpretability of machine learning models by identifying the smallest change to an input required to obtain a desired output.

By Keita Kinjo

safety

arXiv Machine Learning

Aug 7

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.

By Frieder Wizgall, Georg Tirpitz, Moritz Seiler, Kerstin Ritter, B\'alint Mucs\'anyi

benchmarks safety

Related stories

Information-Theoretic Foundations for Machine Learning

A General Framework for Decision Trees via Bregman Divergences

Solution of the Hempel's statistical ambiguity problem and Causal AI

Solution of the Hempel's statistical ambiguity problem and Causal AI

A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluation

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies