The AISTATS 2021 paper entitled “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty” can now be accessed at the following link:

Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty,” 24th International Conference on Artificial Intelligence and Statistics (AISTATS), April 13 – 15, 2021.

In this paper, a strictly concave approximation of MOCU – referred to as “Soft MOCU” – is proposed, which can be used to define an acquisition function for Bayesian active learning with a theoretical convergence guarantee. This study shows that the Soft MOCU based Bayesian active learning outperforms other existing methods, with the important additional benefit of theoretical guarantee of convergence to the optimal classifier.

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