Jul 12, 2020
We study the classical problem of forecasting under logarithmic loss while competing against an arbitrary class of experts. We present a novel approach to bounding the minimax regret that exploits the self-concordance property of logarithmic loss. Our regret bound depends on the metric entropy of the expert class and matches previous best known results for arbitrary expert classes. We improve the dependence on the time horizon for classes with metric entropy under the supremum norm of order Ω(γ^-p) when p>1, which includes, for example, Lipschitz functions of dimension greater than 1.
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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