Online Learning

In many practical applications, the environment is so complex that it may be infeasible to lay out a precise model and use the classical mathematical optimization methods. It is then necessary, and very often beneficial, to consider a robust approach, by considering optimization as a process that learns from experience as more aspects of the problem are being observed. This view of optimization as a process has become prominent in various fields and led to many successes in modeling and systems. One important research direction of our group is to study online learning in various settings, such as convex and submodular utility functions, and subject to full or partial information.

Accessibility at Yale   Inference, Information, and Decision Group at Yale