Our AISTATS 2021 paper entitled “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty” can now be accessed

We are happy to announce that our AISTATS 2021 paper entitled “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty” is now available in the Proceedings of Machine Learning Research, which can 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, we suggest a strictly concave approximation of MOCU – referred to as “Soft MOCU” – that can be used to define an acquisition function for Bayesian active learning with a theoretical convergence guarantee. We show in this study that the Soft MOCU based Bayesian active learning outperforms other existing methods, with the important benefit of theoretical guarantee of convergence to the optimal classifier.