Past talk · AI/ML Seminar Series
Trustworthy Machine Learning in Complex Environments
Assistant Professor of Computer Science, University of Maryland
- Date & time
- Monday, October 10, 2022 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
With the burgeoning use of machine learning models, there is a need to rapidly and reliably deploy models in a variety of environments. These trustworthy models must be able 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, I will cover reinforcement learning algorithms that achieve fast adaptation by guaranteed knowledge transfer, principled methods that measure the vulnerability and improve the robustness of RL agents, and ethical models that make fair decisions under distribution shifts.
About the speaker
Furong Huang is an Assistant Professor of Computer Science at the University of Maryland. She works on statistical and trustworthy machine learning, reinforcement learning, graph neural networks, deep learning theory, and federated learning. She received her Ph.D. from UC Irvine in 2016 and completed a postdoc at Microsoft Research NYC.