ICML 2020 Announces Test of Time Award

Organizers of the 37th International Conference on Machine Learning (ICML) have announced this year's Test of Time award, which goes to a team from the California Institute of Technology, University of Pennsylvania, Saarland University. The ICML Test of Time award recognizes an ICML paper from ten years ago that has proven influential, with significant impacts in the field, "including both research and practice." Authors: Niranjan Srinivas, Andreas Krause, Sham Kakade, Matthias Seeger Institutions: California Institute of Technology, University of Pennsylvania, Saarland University Abstract: Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization.