Efficient and Parsimonious Agnostic Active Learning
This work addresses the need for efficient and robust active learning algorithms in machine learning, though it appears incremental as it builds on existing agnostic approaches.
The paper tackles the problem of active learning in streaming settings by developing a new algorithm that works for any classifier and noise level, is efficiently implementable, and is more aggressive than prior methods, with experimental analysis showing improved performance in various scenarios.
We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previous approaches satisfying 1 and 2. To do this we create an algorithm based on a newly defined optimization problem and analyze it. We also conduct the first experimental analysis of all efficient agnostic active learning algorithms, evaluating their strengths and weaknesses in different settings.