Effective Robustness

On Evaluating Adversarial Robustness

USENIX Security '19 - Lessons Learned from Evaluating the Robustness of Defenses to

Joelle Pineau: Reproducibility, Reusability, and Robustness in Deep Reinforcement Learning ICLR 2018

Steven Van Vaerenbergh

A Data-Centric View On Robustness

Simons Institute

The Robustness of Go • Francesc Campoy • GOTO 2018

GOTO Conferences

Adversarial Robustness

Center for AI Safety

AI Seminar Series: Daniel Brown - Efficient and Robust Learning of Human Intent

Amii

ML Robustness & Engineering - Andrew Ilyas (MIT)

Machine Learning Street Talk

Recent Progress in Adversarial Robustness of AI Models: Attacks, Defenses, and Certification

1Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Simons Institute

SaTML 2023 - Zico Kolter - Robustness in Machine Learning: A Five-Year Retrospective

Nicolas Papernot

Replication, Robustness and Interpretability: Improving How We Communicate Scientific Findings

Women in Data Science Worldwide

Perceived Quality and Perceptual Robustness

Robustness studies: If anything can go wrong, prevent it!

Statistics Made Easy by Stat-Ease

Lessons Learned from Evaluating the Robustness of Defenses to Adversarial Examples

Simons Institute

【EP11】Improving Robustness to Distribution Shifts: Methods and Benchmarks

A Universal Law of Robustness

Sebastien Bubeck

WACV’24 Tutorial on Robustness at Inference: Explainability, Uncertainty, and Intervenability

OLIVES at GATECH

Bo Li (UIUC) - Trustworthy ML: Robustness, Privacy, Generalization, and their Interconnections