Active Learning in the Overparameterized and Interpolating Regime

Mina Karzand, Robert D. Nowak

Published 2019-05-29Version 1

Overparameterized models that interpolate training data often display surprisingly good generalization properties. Specifically, minimum norm solutions have been shown to generalize well in the overparameterized, interpolating regime. This paper introduces a new framework for active learning based on the notion of minimum norm interpolators. We analytically study its properties and behavior in the kernel-based setting and present experimental studies with kernel methods and neural networks. In general, active learning algorithms adaptively select examples for labeling that (1) rule-out as many (incompatible) classifiers as possible at each step and/or (2) discover cluster structure in unlabeled data and label representative examples from each cluster. We show that our new active learning approach based on a minimum norm heuristic automatically exploits both these strategies.