Artificial Intelligence Seminar

— 1:00pm

In Person and Virtual - ET - Newell-Simon 3305 and Zoom

XINYI CHEN , Ph.D. Student, Department of Computer Science, Princeton University

A Nonstochastic Control Approach to Optimization

Selecting the best hyperparameters for a particular optimization instance, such as the learning rate and momentum, is an important but nonconvex problem. As a result, iterative optimization methods such as hypergradient descent lack global optimality guarantees in general. We propose an online nonstochastic control methodology for mathematical optimization. First, we formalize the setting of meta-optimization, an online learning formulation of learning the best optimization algorithm from a class of methods.

The meta-optimization problem over gradient-based methods can be framed as a feedback control problem over the choice of hyperparameters, including the learning rate, momentum, and the preconditioner. Although the original optimal control problem is nonconvex, we show how recent methods from online nonstochastic control using convex relaxations can be used to circumvent the nonconvexity, and obtain regret guarantees vs. the best offline solution. This guarantees that in meta-optimization, given a sequence of optimization problems, we can learn a method that attains convergence comparable to that of the best optimization method in hindsight from a class of methods.

Xinyi Chen is a fourth-year Ph.D. student in the Computer Science department at Princeton University, advised by Prof. Elad Hazan. Her research is at the intersection of online learning, optimization, and control. Previously, she obtained her undergraduate degree from Princeton in Mathematics, where she received the Middleton Miller Prize. She is a recipient of the NSF Graduate Research Fellowship and a participant of EECS Rising Stars at UC Berkeley.

The AI Seminar is generously sponsored by SambaNova Systems.

In Person and Zoom Participation.  See announcement.

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