Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions

Abstract

This paper introduces a new algorithm, Q2, for optimizing the expected output of a multi-input noisy continuous function. Q2 is designed to need only a few experiments, it avoids strong assumptions on the form of the function, and it is autonomous in that it requires little problemspecific tweaking. These capabilities are directly applicable to industrial processes, and may become increasingly valuable elsewhere as the machine learning field expands beyond prediction and function identification, and into embedded active learning subsystems in robots, vehicles and consumer products. Four existing approaches to this problem (response surface methods, numerical optimization, supervised learning, and evolutionary methods) all have inadequacies when the requirement of "black box" behavior is combined with the need for few experiments. Q2 uses instance-based determination of a convex region of interest for performing experiments. In conventional instance-based approaches to learning, a neigh...