Jul 12, 2020
We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are useful in modeling signals with singularities in the domain. For example, BMO functions can model the intensity field of several radioactive emitting sources. A bandit BMO algorithm can help us quickly locate the strongest emitting source. We develop a toolset for BMO bandits, and provide an algorithm that can achieve poly-log δ-regret – a regret measured against an arm that is optimal after removing a δ-sized portion of the arm space.
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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