ECE PhD Prospectus Defense: Jiujia Zhang

Title: Robustifying Online Convex Optimization via Gradient Clipping and Regularization

Presenter: Jiujia Zhang

Advisor: Professor Ashok Cutkosky

Chair: Professor Kayhan Batmanghelich

Committee: Professor Kayhan Batmanghelich, Professor Brian Kulis, Professor Ashok Cutkosky

Google Scholar Link: https://scholar.google.com/citations?hl=en&user=eiOVT-8AAAAJ

Abstract: Contemporary machine learning models are largely trained iteratively with first-order optimization methods that rely on gradients as feedback. Online Convex Optimization (OCO) provides a general framework for designing such algorithms for convex losses under varying degrees of prior knowledge about the problem. Existing methods require exact gradients, but gradients can often be corrupted in practice. For example, model training ty...