Prof. Tara Javidi
Title: Black-box Optimization in Theory and in Practice
Abstract: In this talk, we will consider the problem of maximizing a black-box function via noisy and costly queries from a theoretical perspective (a lot of it) as well as applications (an exciting bit). We first motivate the problem by considering a wide variety of engineering design applications from the heuristic optimization of wireless networks to hardware acceleration to neural network architecture search.
In the second part of the talk, we consider the problem in a Bayesian framework with a Gaussian Process prior. In particular, a new algorithm for this problem is proposed, and high probability bounds on its simple and cumulative regret are established. The query point selection rule in most existing methods involves an exhaustive search over an increasingly fine sequence of uniform discretizations of the input space. The proposed algorithm, in contrast, adaptively refines the domain which leads to a lower computational complexity, particularly when the domain is a subset of a high dimensional Euclidean space. In addition to the computational gains, sufficient conditions are identified under which the regret bounds of the new algorithm improve upon the known results.
In the last part of the talk, we build on the intuition provided by our work in the Bayesian setting to consider the problem in a non-Bayesian setting where the objective function is assumed to have a smooth kernel representation. Most notably the proposed algorithm –augmenting the Gaussian Process surrogate with a local polynomial estimator— closes a significant gap to the (optimal) regret lower bound for a class of widely used and practically relevant Matern family of Kernels. This is joint work with my PhD student Shekhar Shubhanshu.
Biography: Dr. Javidi is a professor of electrical and computer engineering at University of California, San Diego. She received her MS and PhD degrees in Electrical Engineering and Computer Science as well as her MS in Applied Mathematics from the University of Michigan, Ann Arbor. Before joining UCSD, she was on the faculty of Electrical Engineering Department at the University of Washington, Seattle; In 2013-2014, she spent her sabbatical at Stanford University as a visiting faculty. Her area of research is at the intersection of stochastic control, information theory, and data science with notable contributions to the theory of information acquisition and active learning. At the University of California, San Diego, Tara is a founding co-director of the Center for Machine-Integrated Computing and Security, the principal investigator of Detect Drone Project as well as a faculty member of the member of the Centers of Information Theory and Applications (ITA), Halıcıoğlu Data Science Institute, Wireless Communications (CWC), Contextual Robotics Institute (CRI) and Networked Systems (CNS).