arXiv Machine Learning June 5, 2026 By August Y. Chen, Ahmed El Alaoui

How abundant are good interpolators?

Read the original on arXiv Machine Learning →

arXiv:2606. 06469v1 Announce Type: cross Abstract: Let $S$ be the set of unit norm linear classifiers $\theta \in \mathbb{R}^d$ which correctly classify every point of a labeled dataset $(X_i,y_i)_{i=1}^n$, $X_i \in \mathbb{R}^d$, $y_i \in \{-1,+1\}$, with a possibly negative margin $\kappa$ fixed in advance.

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

arXiv Machine Learning

Jun 30

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.

By Andrea Montanari, Kangjie Zhou

arXiv AI

Jun 4

Does Order Matter : Connecting The Law of Robustness to Robust Generalization

arXiv:2602. 20971v3 Announce Type: replace-cross Abstract: Bubeck and Selke (2021) propose the connection between the Law of Robustness and robust generalization error as an open problem.

By Mihir More, Aritra Das, Jaee Ponde, Himadri Mandal, Vishnu Varadarajan, Debayan Gupta

arXiv Machine Learning

Aug 7

Optimal Rates for Learning with Monotone Adversaries

arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.

By Anay Mehrotra

arXiv Machine Learning

Aug 4

The Fourth Quadrant: A Stylized View of Benign Misfitting

arXiv:2608. 01032v1 Announce Type: new Abstract: Training error is what we can observe on a training set; test error is the quantity we actually care about.

By Gireeja Ranade, Anant Sahai

safety

arXiv Machine Learning

Jul 2

Fast Score-Based Sampling via Log-Concave Reductions

arXiv:2512. 24152v2 Announce Type: replace-cross Abstract: Sampling based on score diffusions has led to striking empirical results, and has attracted considerable attention from various research communities.

By M. J. Wainwright

arXiv Machine Learning

Jun 8

Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization

arXiv:2604. 03146v2 Announce Type: replace-cross Abstract: We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs.

By Chiheb Yaakoubi, Cosme Louart, Malik Tiomoko, Zhenyu Liao

Related stories

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

Does Order Matter : Connecting The Law of Robustness to Robust Generalization

Optimal Rates for Learning with Monotone Adversaries

The Fourth Quadrant: A Stylized View of Benign Misfitting

Fast Score-Based Sampling via Log-Concave Reductions

Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization