Machine Learning at the Flatiron Institute Seminar: Tim Rudner

Date & Time

Title: Data-Driven Priors for Trustworthy Machine Learning

Abstract: Machine learning models, while effective in controlled environments, can fail catastrophically when exposed to unexpected conditions upon deployment. This lack of robustness, well-documented even in state-of-the-art models, can lead to severe harm in high-stakes, safety-critical application domains such as healthcare. This shortcoming raises a central question: How can we develop machine learning models we can trust?
In this talk, I will approach this question from a probabilistic perspective and address deficiencies in the trustworthiness of neural network models using Bayesian principles. Specifically, I will show how to improve the reliability and fairness of neural networks with data-driven domain-informed prior distributions over model parameters. To do so, I will first demonstrate how to perform training in neural networks with such priors using a simple variational optimization objective with a regularizer that reflects the constraints implicitly encoded in the prior. This regularizer is mathematically simple, easy to implement, and can be used as a drop-in replacement for existing regularizers when performing supervised learning in neural networks of any size. I will then show how to construct and use domain-informed data-driven priors to improve uncertainty quantification and group robustness in neural network models for selected application domains.

About the Speaker

Tim G. J. Rudner is a Data Science Assistant Professor and Faculty Fellow at New York University’s Center for Data Science and an AI Fellow at Georgetown University’s Center for Security and Emerging Technology. He conducted PhD research on probabilistic machine learning in the Department of Computer Science at the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal. The goal of his research is to create trustworthy machine learning models by developing methods and theoretical insights that improve the reliability, safety, transparency, and fairness of machine learning systems deployed in safety-critical settings. Tim holds a master’s degree in statistics from the University of Oxford and an undergraduate degree in applied mathematics and economics from Yale University. He is also a Qualcomm Innovation Fellow and a Rhodes Scholar.

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