arXiv Machine Learning September 17, 2026 By Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma

Reliable learning in challenging environments

Read the original on arXiv Machine Learning →

The paper addresses the challenge of creating machine learning learners that can guarantee provably correct predictions in difficult test-time scenarios, such as adversarial attacks and natural distribution shifts. It introduces a reliable learner with optimal theoretical guarantees for these settings and discusses practical implementations. The authors demonstrate strong performance on examples like linear separators under log-concave distributions and smooth boundary classifiers under smooth probability distributions.

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.

safety

arXiv Machine Learning

Jun 25

Learning with Monotone Adversarial Corruptions

arXiv:2601. 02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversarial corruption model.

By Kasper Green Larsen, Chirag Pabbaraju, Abhishek Shetty

safety

arXiv Machine Learning

Jul 7

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.

By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt

benchmarks safety

arXiv AI

Jul 1

Improving Certified Robustness via Adversarial Distillation

arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.

By Matteo Melis, Jesus Martinez Del Rincon, Vishal Sharma

efficiency benchmarks safety

arXiv Machine Learning

Jun 2

A unifying Bayesian framework for adversarial robustness

arXiv:2510. 09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge.

By Pablo G. Arce, Roi Naveiro, David R\'ios Insua

benchmarks safety

arXiv Machine Learning

Jun 8

Certified Robustness to Data Poisoning in Gradient-Based Training

arXiv:2406. 05670v3 Announce Type: replace Abstract: Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks.

By Philip Sosnin, Mark N. M\"uller, Maximilian Baader, Calvin Tsay, Matthew Wicker

agents robotics data-engineering

arXiv AI

Jun 2

CEAR: Certified Ensemble Adversarial Robustness in DNNs

arXiv:2606. 01437v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are highly susceptible to adversarial perturbations, leading to extensive research on robustness for safety-critical applications.

By Daniel Sadig, Mohammadreza Maleki, Hamed Karimi, Reza Samavi

benchmarks safety

One email a morning, machine-written

Related stories

Learning with Monotone Adversarial Corruptions

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

Improving Certified Robustness via Adversarial Distillation

A unifying Bayesian framework for adversarial robustness

Certified Robustness to Data Poisoning in Gradient-Based Training

CEAR: Certified Ensemble Adversarial Robustness in DNNs