Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens

Understand

In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification.

• By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm.
• Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point.
• The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits.