Web: http://arxiv.org/abs/2209.07454

Sept. 16, 2022, 1:12 a.m. | Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, Nicola Gatti

cs.LG updates on arXiv.org arxiv.org

We study online learning problems in which a decision maker has to take a
sequence of decisions subject to $m$ long-term constraints. The goal of the
decision maker is to maximize their total reward, while at the same time
achieving small cumulative constraints violation across the $T$ rounds. We
present the first best-of-both-world type algorithm for this general class of
problems, with no-regret guarantees both in the case in which rewards and
constraints are selected according to an unknown stochastic …

arxiv constraints framework long-term optimization

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