Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. (arXiv:2206.07908v1 [cs.LG])

Web: http://arxiv.org/abs/2206.07908

The problem of online learning with graph feedback has been extensively
studied in the literature due to its generality and potential to model various
learning tasks. Existing works mainly study the adversarial and stochastic
feedback separately. If the prior knowledge of the feedback mechanism is
unavailable or wrong, such specially designed algorithms could suffer great
loss. To avoid this problem, \citet{erez2021towards} try to optimize for both
environments. However, they assume the feedback graphs are undirected and each
vertex has a …

arxiv feedback general graph learning lg stochastic