Roman Garnett, Assistant Professor of Computer Science & Engineering at Washington Univesity, will present.
Active Search for Automating Scientific Discovery
Abstract: With the explosive growth of the internet and availability of increasingly cheap large-scale storage, amassing huge datasets is becoming commonplace. In many real-world scenarios, however, conducting detailed analysis of an identified data point is very expensive, requiring human intervention or a costly experiment. In such situations, it is critical that we allocate limited resources effectively. In "active" machine learning, we consider how to collect the most-useful data to achieve our goals effectively with a limited budget for labeling. We will introduce active machine learning and discuss a particular important setting: "active search," where we seek to discover rare, valuable points from a large database of alternatives. We will discuss the surprising difficulty of this problem and introduce efficient, nonmyopic polices to solve it, demonstrating our method on large-scale drug and materials discovery simulations.
Bio: Roman Garnett has been an assistant professor in the Computer Science & Engineering department since 2015. His research seeks to accelerate scientific discovery through intelligent Bayesian algorithms for sequential experimental design. He recently received the NSF CAREER award for this effort.
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