Efficient Active Learning for Gaussian Process Classification by Error Reduction

Journal Article · Mon Dec 06 04:00:00 EST 2021 · Advances in Neural Information Processing Systems

OSTI ID:1842011

Zhao, Guang ^[1]; Dougherty, Edward R. ^[1]; Yoon, Byung-Jun ^[2]; Alexander, Francis ^[3]; Qian, Xiaoning ^[2]

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1. Texas A & M Univ., College Station, TX (United States)
2. Texas A & M Univ., College Station, TX (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
3. Brookhaven National Lab. (BNL), Upton, NY (United States)

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)
• Grant/Contract Number: SC0012704; SC0019303
• OSTI ID: 1842011
• Report Number(s): BNL-222619-2022-JAAM
• Journal Information: Advances in Neural Information Processing Systems, Journal Name: Advances in Neural Information Processing Systems Vol. 34; ISSN 1049-5258
• Publisher: Association for Computing Machinery (ACM)
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Bayesian Active Learning
Expected Error Reduction
Gaussian Process Classification
Query Synthesis