Our NeurIPS 2021 paper entitled “Efficient Active Learning for Gaussian Process Classification by Error Reduction” is now available online at the following link: https://openreview.net/pdf?id=UK15Hj9qX6I
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Efficient Active Learning for Gaussian Process Classification by Error Reduction,” Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS 2021), Dec. 6 – 14, 2021.
In this paper, we investigate active learning scenarios for Gaussian Process Classification (GPC), where we develop computationally efficient algorithms for EER (expected error reduction)-based active learning with GPC. In particular, we consider EER as the reduction of the Mean Objective Cost of Uncertainty (MOCU), where the learning objective of GPC is to reduce the classification error.
Our experiments clearly demonstrate the computational efficiency of the proposed approach and performance evaluation of our algorithms on both synthetic and real-world datasets show that they significantly outperform existing state-of-the-art algorithms in terms of sampling efficiency.
Objective-UQ 