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Found 3 papers, 1 papers with code
Be Greedy in Multi-Armed Bandits
On the other hand, this heuristic performs reasonably well in practice and it even has sublinear, and even near-optimal, regret bounds in some very specific linear contextual and Bayesian bandit models.
Lifelong Learning in Multi-Armed Bandits
Continuously learning and leveraging the knowledge accumulated from prior tasks in order to improve future performance is a long standing machine learning problem.
Categorized Bandits
We introduce a new stochastic multi-armed bandit setting where arms are grouped inside ``ordered'' categories.
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