Pool-Based Agnostic Experiment Design in Linear Regression

Masashi Sugiyama, Shinichi Nakajima

Abstract: We address the problem of batch active learning (or experiment design) in regression scenarios, where the best input points to label is chosen from a ‘pool’ of unlabeled input samples. Existing active learning methods often assume that the model is correctly specified, i.e., the unknown learning target function is included in the model at hand. However, this assumption may not be fulfilled in practice (i.e., agnostic) and then the existing methods do not work well. In this paper, we propose a new active learning method that is robust against model misspecification. Simulations with various benchmark datasets as well as a real application to wafer alignment in semiconductor exposure apparatus illustrate the usefulness of the proposed method.