Robust Bayesian Classification Using An Optimistic Score Ratio

Jul 12, 2020

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We consider the optimistic score ratio for robust Bayesian classification when the class-conditional distribution of the features is not perfectly known. The optimistic score searches for the distribution that is most plausible to explain the observed test sample among all distributions belonging to the class-dependent ambiguity set which is prescribed using a moment-based divergence. We show that the classification approach using optimistic score ratio is conceptually attractive, delivers rigorous statistical guarantees and is computationally tractable. We showcase the power of the proposed optimistic score ratio classifier on both synthetic and empirical data.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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