Web: http://arxiv.org/abs/2205.04860

May 11, 2022, 1:11 a.m. | Ativ Joshi, Abhishek Sinha

cs.LG updates on arXiv.org arxiv.org

In the learning literature, the performance of an online policy is commonly
measured in terms of the static regret metric, which compares the cumulative
loss of an online policy to that of an optimal benchmark in hindsight. In the
definition of static regret, the benchmark policy remains fixed throughout the
time horizon. Naturally, the resulting regret bounds become loose in
non-stationary settings where fixed benchmarks often suffer from poor
performance. In this paper, we investigate a stronger notion of regret …


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