Nguyen, V.A., Si, N. & Blanchet, J.. (2020). Robust Bayesian Classification Using An Optimistic Score Ratio. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:7327-7337 Available from https://proceedings.mlr.press/v119/nguyen20e.html.
Abstract
We build a Bayesian contextual classification model using an optimistic score ratio for robust binary classification when there is limited information on the class-conditional, or contextual, distribution. The optimistic score searches for the distribution that is most plausible to explain the observed outcomes in the testing sample among all distributions belonging to the contextual ambiguity set which is prescribed using a limited structural constraint on the mean vector and the covariance matrix of the underlying contextual distribution. We show that the Bayesian classifier using the optimistic score ratio is conceptually attractive, delivers solid statistical guarantees and is computationally tractable. We showcase the power of the proposed optimistic score ratio classifier on both synthetic and empirical data.
Authors
Viet Anh Nguyen, Nian Si, Jose Blanchet
Publication date
2020/11/21
Conference
International Conference on Machine Learning
Pages
7327-7337
Publisher
PMLR
