Events Calendar

PhD Thesis Proposal - Michael Tsang

Title: Interpretable Machine Learning Models via Feature Interaction Discovery
Date/Time: Thursday, November 14th 10-11:30am
Location: SAL 322
Candidate: Michael Tsang
Committee: Prof. Yan Liu (adviser), Prof. Joseph Lim, Prof. Maja Mataric, Prof. Emily Putnam Hornstein, Prof. Xiang Ren


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 thesis proposal, we advance these directions via the problem of feature interaction discovery. We develop a way to interpret the feature interactions in feedforward neural networks by tracing their learned weights. We follow-up on this method and develop a way of learning transparent neural networks. Lastly, we investigate applications of this work on interpreting black-box models beyond feedforward neural networks, such as image/text classifiers and recommender systems. Throughout this presentation, we will explain the physical meaning and practical importance of our feature interaction interpretations.

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Lizsl De Leon