Machine Learning Robustness, Fairness, And Their Convergence

Machine Learning Robustness, Fairness, and their Convergence (ACM SIGKDD 2021 Tutorial Part.01)

Machine Learning Robustness, Fairness, and their Convergence (ACM SIGKDD 2021 Tutorial Part.04)

Machine Learning Robustness, Fairness, and their Convergence (ACM SIGKDD 2021 Tutorial Part.03)

Machine Learning Robustness, Fairness, and their Convergence (ACM SIGKDD 2021 Tutorial Part.02)

Robustness in Machine Learning Explanations: Does it Matter?

ACM FAccT Conference

Prof. Asu Ozdaglar - Robustness in Machine Learning and Optimization: A Minmax Approach

Robustness of Fairness in Machine Learning

Michigan Engineering - Honors Program

Machine learning fairness and adversarial learning

Robustness and Strategic Concerns in Machine Learning - Yu Cheng

Institute for Advanced Study

Fairness and robustness in machine learning – a formal methods perspective - Aditya Nori, Microsoft

The Alan Turing Institute

ICLR Paper: Training Individual Fair ML Models with Sensitive Substance Robustness

Scalable and Robust Deep Learning: Ali Ramezani-Kebrya (UiO)

SFI Visual Intelligence

Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness

Toronto Machine Learning Series (TMLS)

Stanford Fireside Talks: Robustness in Machine Learning I Robust Machine Learning

Stanford Online

SML201 Fairness in Machine Learning

Michael Guerzhoy

Fairness in Machine Learning

Optimizing Fairness and Robustness in Machine Learning Models

Probabilistic Methods for Increased Robustness in Machine Learning - Jose Miguel Hernandez Lobato

The Alan Turing Institute

AI Seminar Series - #2 | Robustness in Machine Learning and Optimization: A Minmax Approach |