Efficient and Parsimonious Agnostic Active Learning

By Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, J. Langford, Robert E. Schapire. 2015

49 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient (2017, methodology): “In [19], a computationally efficient algorithm for disagreement based AL.”
Practical Evaluation and Optimization of Contextual Bandit Algorithms (2018, methodology): “Similar conditions have been frequently used in supervised learning (Massart et al., 2006) and active learning (Hsu, 2010; Huang et al., 2015; Krishnamurthy et al., 2017) for obtaining better data-dependent guarantees.”
A Contextual Bandit Bake-off (2018, methodology): “A similar quantity has been used, e.g. , by Huang et al. (2015); Karampatziakis and Langford (2011); Krishnamurthy et al. (2019).”