arXiv Machine Learning August 7, 2026 By Dan Tsir Cohen, Steve Hanneke, Aryeh Kontorovich

Realizable Bayes-Consistency for General Metric Losses

Read the original on arXiv Machine Learning →

arXiv:2605. 03823v3 Announce Type: replace Abstract: We study strong universal Bayes-consistency in the realizable setting for learning with general metric losses, extending classical characterizations beyond $0$-$1$ classification (Bousquet et al.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 11

Optimal Learning Under Tsybakov Noise

arXiv:2608. 08416v1 Announce Type: new Abstract: Probably Approximately Correct (PAC) learning [Val84] is a fundamental learning model that has been extensively investigated.

By Steve Hanneke, Hongao Wang, Mingyue Xu

arXiv Machine Learning

Jun 16

Online Realizable Regression and Applications for ReLU Networks

arXiv:2602. 19172v2 Announce Type: replace Abstract: Realizable online regression can behave very differently from online classification.

By Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel

safety

Hugging Face Trending Papers

Sep 21

Adversarially Robust PAC Learning with Optimal VC Rates

We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in clas...

safety

arXiv Machine Learning

Sep 22

Adversarially Robust PAC Learning with Optimal VC Rates

arXiv:2609.24260v1 Announce Type: cross Abstract: We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distrib...

By Steve Hanneke, Amirreza Shaeiri

safety

arXiv Machine Learning

Sep 1

A Borel Concept Class of VC Dimension One with a Non-PAC Consistent Learner in ZFC

arXiv:2608.30246v1 Announce Type: cross Abstract: The fundamental theorem of statistical learning states that, under suitable measurability assumptions, finite Vapnik--Chervonenkis (VC) dimension gua...

By Mateus Jesus de Arruda Campos, Gabriel Fernandes, Vinicius de Oliveira Rodrigues

arXiv Machine Learning

Jul 27

Encoding orders and trees in real-valued functions

arXiv:2607. 21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a binary relation.

By G Conant, C Terry

One email a morning, machine-written

Related stories

Optimal Learning Under Tsybakov Noise

Online Realizable Regression and Applications for ReLU Networks

Adversarially Robust PAC Learning with Optimal VC Rates

Adversarially Robust PAC Learning with Optimal VC Rates

A Borel Concept Class of VC Dimension One with a Non-PAC Consistent Learner in ZFC

Encoding orders and trees in real-valued functions