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Adaptive Sampling for Discovery
Ziping Xu · Eunjae Shim · Ambuj Tewari · Paul Zimmerman

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #518

In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses.This problem has wide applications to real-world discovery problems, for example drug discovery with the help of machine learning models. ASD algorithms face the well-known exploration-exploitation dilemma. The algorithm needs to choose points that yield information to improve model estimates but it also needs to exploit the model. We rigorously formulate the problem and propose a general information-directed sampling (IDS) algorithm. We provide theoretical guarantees for the performance of IDS in linear, graph and low-rank models. The benefits of IDS are shown in both simulation experiments and real-data experiments for discovering chemical reaction conditions.

Author Information

Ziping Xu (University of Michigan)

My name is Ziping Xu. I am a fifth-year Ph.D. student in Statistics at the University of Michigan. My research interests are on sample efficient reinforcement learning and transfer learning, multitask learning. I am looking for research-orientated full-time job starting Fall 2023

Eunjae Shim (University of Michigan - Ann Arbor)
Ambuj Tewari (University of Michigan)
Paul Zimmerman (University of Michigan)

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