Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery

TL;DR

This paper introduces a novel active search framework for multiple target classes that balances diversity and discovery efficiency using a utility function with diminishing returns, supported by theoretical analysis and empirical validation.

Contribution

It formulates a new active search problem with multiple classes and a flexible utility function, providing an efficient approximation algorithm and empirical results.

Findings

The proposed method outperforms baselines in diverse discovery tasks.

The utility function with diminishing returns effectively balances diversity and discovery.

Theoretical hardness results highlight challenges in approximating optimal policies.

Abstract

Active search is a setting in adaptive experimental design where we aim to uncover members of rare, valuable class(es) subject to a budget constraint. An important consideration in this problem is diversity among the discovered targets -- in many applications, diverse discoveries offer more insight and may be preferable in downstream tasks. However, most existing active search policies either assume that all targets belong to a common positive class or encourage diversity via simple heuristics. We present a novel formulation of active search with multiple target classes, characterized by a utility function chosen from a flexible family whose members encourage diversity via a diminishing returns mechanism. We then study this problem under the Bayesian lens and prove a hardness result for approximating the optimal policy for arbitrary positive, increasing, and concave utility functions. Finally, we design an efficient, nonmyopic approximation to the optimal policy for this class of utilities and demonstrate its superior empirical performance in a variety of settings, including drug discovery.