Jun 14, 2019
This workshop aims to bring together researchers from industry and academia in order to describe recent advances and discuss future research directions pertaining to real-world sequential decision making, broadly construed. We aim to highlight new and emerging research opportunities for the machine learning community that arise from the evolving needs for making decision making theoretically and practically relevant for realistic applications. Research interest in reinforcement and imitation learning has surged significantly over the past several years, with the empirical successes of self-playing in games and availability of increasingly realistic simulation environments. We believe the time is ripe for the research community to push beyond simulated domains and start exploring research directions that directly address the real-world need for optimal decision making. We are particularly interested in understanding the current theoretical and practical challenges that prevent broader adoption of current policy learning and evaluation algorithms in high-impact applications, across a broad range of domains. This workshop welcomes both theory and application contributions.
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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