Parameter-Free, Dynamic, and Strongly-Adaptive Online Learning

Jul 12, 2020



We provide a new online learning algorithm that for the first time combines several disparate notions of adaptivity. First, our algorithm obtains a “parameter-free” regret bound that adapts to the norm of the comparator and the squared norm of the size of the gradients it observes. Second, it obtains a “strongly-adaptive” regret bound, so that for any given interval of length N, the regret over the interval is Õ(√(N)). Finally, our algorithm obtains an optimal “dynamic” regret bound: for any sequence of comparators with path-length P, our algorithm obtains regret Õ(√(PN)) over intervals of length N. Our primary technique for achieving these goals is a new method of combining constrained online learning regret bounds that does not rely on an expert meta-algorithm to aggregate learners.



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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