This talk has ended.

The rise of machine learning (ML) and deep learning has revolutionized almost every aspect of our daily life. Learning-based methods are now widely used in financial, medical, and legal applications for tasks involving not only predictions, but also decision making, often in adversarial, non-stationary, and strategic environments, and sometimes relying on sensitive data. Classical statistical learning theory does not cover these new settings, which motivates us to develop new theories and algorithms for applying ML responsibly in these emerging applications.

In this talk, I will cover recent advances from that address these challenges with strong theoretical guarantees. Topics include new technical results in offline reinforcement learning, adaptive online learning and differential privacy as well as their promise in real-life applications.