We are happy to announce that our ICLR 2021 paper below can now be accessed online on OpenReview.net:
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Uncertainty-aware Active Learning for Optimal Bayesian Classifier,” 9th International Conference on Learning Representations (ICLR), May 4-8, 2021.
In this paper, we propose an acquisition function for active learning of a Bayesian classifier based on a weighted form of MOCU (mean objective cost of uncertainty). By quantifying the uncertainty that directly affects the classification error, the proposed method avoids the shortcoming of the previous expected Loss Reduction (ELR) methods by avoiding their myopic behavior. Unlike existing ELR methods, which may get stuck before reaching the optimal classifier, the proposed weighted-MOCU based strategy provides the critical advantage that the resulting Bayesian active learning algorithm guarantees convergence to the optimal classifier of the true model. We demonstrate its performance with both synthetic and real-world datasets.
Objective-UQ 