Adaptive Learning for Discovery

• URL: http://arxiv.org/abs/2205.14829v1
• Date: Mon, 30 May 2022 03:30:45 GMT
• Title: Adaptive Learning for Discovery
• Authors: Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman
• Abstract summary: We study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD) ASD algorithms 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.
• Score: 18.754931451237375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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