Hi! I am an Assistant Professor in the Department of Computer Sciences, University of Illinois at Chicago. I completed my PhD in UW Madison in August 2019.
Our recent work describes cheap methods to train deep networks with explicit constraints: we call it Deep-Conditional Gradient (Deep-CG). Our paper provides compelling evidence supporting three distinct but closely related threads:
Intuitively, a coreset is a subset of the original dataset which behaves "almost" like the entire dataset, that is, any (relevant) statistic computed using a coreset will be provably close to the quantity if computed using the entire dataset. In this work, the goal was to evaluate whether CG type methods can be derived for such nonsmooth problems in ML. This paper showed that by bringing together with the ε-subdifferential and approximate subdifferential, there does exist a CG type algorithm for such problems. Using this construction, we were also able to analyze the sparsity of the solution at a given iteration, thus computing the coreset simultaneously.
While the recent developments in computer vision algorithms driving a range of turnkey applications is exciting, an evolving body of works have concurrently shown the fragility of the algorithms, specifically with respect to random perturbations. For instance, a state of the art vision system can be fooled into thinking that a "Stop" sign is a "Yield" sign simply by putting a tiny sticker on it. I am very interested in the systematic study of the resilience of vision algorithms and using the results or insights obtained to design better algorithms.