Thu, Jun 11, 2020 @ 02:00 PM - 04:00 PM
Ph.D. Defense - Michael Tsang 6/11 2:00 pm "Interpretable Machine Learning Models via Feature Interaction Discovery"
Ph.D. Candidate: Michael Tsang
Date: Thursday, June 11, 2020
Time: 2:00 PM - 4:00 PM
Committee: Yan Liu (Chair), Emily Putnam-Hornstein, Xiang Ren
Title: Interpretable Machine Learning Models via Feature Interaction Discovery
The impact of machine learning prediction models has created a growing need for us to understand why they make their predictions. The interpretation of these models is important to reveal their fundamental behavior, to obtain scientific insights into data, and to help us trust automatic predictions. In this dissertation, we examine how to explain black-box prediction models via feature interaction detection and attribution, i.e. if features influence each other and how these interactions contribute to predictions, respectively.
We first discuss how feature interaction detection leads to model interpretations of diverse domains such as image/text classification and automatic recommendation. Here, we focus on the special case of recommendation where interaction detection improves not only model interpretability but also prediction performance. We then discuss how to attribute predictions to feature interactions in a way that is simultaneously interpretable, model-agnostic, principled, and scalable. Our discussion culminates in the unification of interaction detection and attribution to yield general prediction visualizations that are both intuitive and insightful.
Meeting ID: 566 970 4161
Google Meet (ONLY A BACKUP - IF WE EXPERIENCE PROBLEMS WITH ZOOM):
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PIN:455 863 061
WebCast Link: https://usc.zoom.us/j/5669704161
Audiences: Everyone Is Invited
Contact: Lizsl De Leon