Adaptive Sampling for Discovery

TL;DR

This paper introduces the Adaptive Sampling for Discovery (ASD) problem, proposing an information-directed sampling algorithm with theoretical guarantees, demonstrated through simulations and real-world chemical discovery experiments.

Contribution

It formulates ASD rigorously and develops a general IDS algorithm with proven performance guarantees across multiple models.

Findings

IDS outperforms baseline methods in simulations

IDS achieves significant improvements in chemical discovery tasks

Theoretical guarantees validate IDS effectiveness

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.