Adaptive Sampling for Discovery
This work addresses a sequential decision-making problem with applications in domains like drug discovery, but it appears incremental as it builds on existing exploration-exploitation methods.
The paper tackles the Adaptive Sampling for Discovery (ASD) problem, where algorithms sequentially label points from an unlabeled dataset to maximize response sums, such as in drug discovery, by proposing an information-directed sampling (IDS) algorithm and demonstrating its benefits in simulations and real-data experiments for chemical reaction conditions.
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.