Influence Diagram Bandits

Jul 12, 2020

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We propose a novel framework for structured bandits, which we call influence diagram bandit. Our framework captures complicated statistical dependencies between actions, latent variables, and observations; and unifies and extends many existing models, such as combinatorial semi-bandits, cascading bandits, and low-rank bandits. We develop novel online learning algorithms that allow us to act efficiently in our models. The key idea is to track a structured posterior distribution of model parameters, either exactly or approximately. To act, we sample model parameters from their posterior and then use the structure of the influence diagram to find the most optimistic actions under the sampled parameters. We experiment with three structured bandit problems: cascading bandits, online learning to rank in the position-based model, and rank-1 bandits. Our algorithms achieve up to about 3 times higher cumulative reward than baselines.

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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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