Calvin Lab Auditorium
Empirical risk minimization (ERM) is a canonical learning algorithm for learning a hypothesis with small generalization error. Moreover, there exists several generic results that characterize the generalization error in terms of hypotheses space and other problem specific characteristics.
However, most of these results focus on non-private learning where the learned hypothesis might reveal significant information about individual points. In this talk, we will survey some of the recent results for privacy-preserving ERM. In particular, we will discuss privacy and generalization error guarantees of these methods and also discuss their pros/cons in terms of theoretical bounds as well as empirical results.
The talk is based on joint works with Abhradeep Guha Thakurta and Pravesh Kothari.