Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation

• URL: http://arxiv.org/abs/2202.06881v1
• Date: Mon, 14 Feb 2022 17:15:18 GMT
• Title: Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
• Authors: Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
• Abstract summary: We propose Active Surrogate Estimators (ASEs) for model evaluation. We find that ASEs offer greater label-efficiency than the current state-of-the-art.
• Score: 59.7305309038676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Active Surrogate Estimators (ASEs), a new method for label-efficient model evaluation. Evaluating model performance is a challenging and important problem when labels are expensive. ASEs address this active testing problem using a surrogate-based estimation approach, whereas previous methods have focused on Monte Carlo estimates. ASEs actively learn the underlying surrogate, and we propose a novel acquisition strategy, XWING, that tailors this learning to the final estimation task. We find that ASEs offer greater label-efficiency than the current state-of-the-art when applied to challenging model evaluation problems for deep neural networks. We further theoretically analyze ASEs' errors.

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