Low-Regret Active learning

TL;DR

This paper introduces an online active learning algorithm based on regret minimization that efficiently identifies informative data points, outperforming greedy methods and uniform sampling in noisy real-world scenarios.

Contribution

It formulates active learning as a sleeping experts problem and provides a low-regret, computationally efficient algorithm that competes with ensemble-based methods.

Findings

Outperforms greedy active learning methods in empirical tests.

Consistently beats uniform sampling on real-world datasets.

Achieves low regret on easy instances while handling adversarial noise.

Abstract

We develop an online learning algorithm for identifying unlabeled data points that are most informative for training (i.e., active learning). By formulating the active learning problem as the prediction with sleeping experts problem, we provide a regret minimization framework for identifying relevant data with respect to any given definition of informativeness. Motivated by the successes of ensembles in active learning, we define regret with respect to an omnipotent algorithm that has access to an infinity large ensemble. At the core of our work is an efficient algorithm for sleeping experts that is tailored to achieve low regret on easy instances while remaining resilient to adversarial ones. Low regret implies that we can be provably competitive with an ensemble method \emph{without the computational burden of having to train an ensemble}. This stands in contrast to state-of-the-art active learning methods that are overwhelmingly based on greedy selection, and hence cannot ensure good performance across problem instances with high amounts of noise. We present empirical results demonstrating that our method (i) instantiated with an informativeness measure consistently outperforms its greedy counterpart and (ii) reliably outperforms uniform sampling on real-world scenarios.