Trustworthy Machine Learning in Complex Environments

Dr. Furong Huang

Assistant Professor of the Department of Computer Science

University of Maryland

1/27/2023

11AM – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=m9ab3786b04dc901b8cd82f477b43ddc3

Abstract: With the burgeoning use of machine learning models in an assortment of applications, there is a need to rapidly and reliably deploy models in a variety of environments. These trustworthy machine learning models must satisfy certain criteria, namely the ability to: (i) adapt and generalize to previously unseen worlds although trained on data that only represent a subset of the world, (ii) allow for non-iid data, (iii) be resilient to (adversarial) perturbations, and (iv) conform to social norms and make ethical decisions. In this talk, towards trustworthy and generally applicable intelligent systems, I will cover some reinforcement learning algorithms that achieve fast adaptation by guaranteed knowledge transfer, principled methods that measure the vulnerability and improve the robustness of reinforcement learning agents, and ethical models that make fair decisions under distribution shifts.