Monday, September 16, 20191:00 PM - 2:00 PMCNLS Conference Room (TA-3, Bldg 1690)|
Detecting Bugs and Explaining Predictions of Machine Learning Models
Sameer SinghUniversity of California, Irvine
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or detect bugs in their behavior. For example, determining when a machine learning model is “good enough” is challenging since held-out accuracy metrics significantly overestimate real-world performance.In this talk, I will describe our research on approaches that explain the predictions of any classifier in an interpretable and faithful manner, and automated techniques to detect bugs that can occur naturally when a model is deployed. In particular, these methods describe the relationship between the components of the input instance and the classifier’s prediction. I will cover various ways in which we summarize this relationship: as linear weights, as precise rules, and as counter-examples, and present experiments to contrast them and evaluate their utility in understanding, and debugging, black-box machine learning algorithms, on tabular, image, text, and graph completion applications. **This seminar is part of a series on Artificial Intelligence for Computational Science.
Host: Aric Hagberg