Adnan Darwiche, University of California, Los Angeles

Abstract

I will present a theory for reasoning about the decisions made by AI systems, particularly classifiers such as decision trees, random forests, Bayesian networks and some limited types of neural networks. The theory is based on “compiling" the input-output behavior of classifiers into discrete functions in the form of tractable circuits. At the heart of the theory is the notion of “complete reason” behind a decision which is extracted from a circuit-instance pair and can be used to answer many queries about the decision, including ones pertaining to explainability, robustness and bias. I will also overview developments on tractable circuits which provide the computational arm for employing this theory in practice and will briefly overview recent results on quantified Boolean logic which provide classifier-independent semantics of this theory that further broadens its applicability. 

Bio:

Adnan Darwiche is a professor and former chairman of the computer science department at UCLA. He directs the Automated Reasoning Group, which focuses on symbolic reasoning, probabilistic reasoning and their applications to machine learning. Professor Darwiche is Fellow of AAAI and ACM and recipient of the Lockheed Martin Excellence in Teaching Award. He is a former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and author of "Modeling and Reasoning with Bayesian Networks," by Cambridge University Press. 

Link to video: https://www.youtube.com/watch?v=qK-znsfl66E

This talk is hosted by David Pennock, DIMACS Director

Presented via zoom: https://rutgers.zoom.us/j/96777068563

Meeting ID: 967 7706 8563

Password: tripods22

Presented in association with the DATA-INSPIRE TRIPODS Institute.