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Found 4 papers, 1 papers with code
$(ε, u)$-Adaptive Regret Minimization in Heavy-Tailed Bandits
In this setting, we study the regret minimization problem when $\epsilon$ and $u$ are unknown to the learner and it has to adapt.
A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints
We study online learning in episodic constrained Markov decision processes (CMDPs), where the goal of the learner is to collect as much reward as possible over the episodes, while guaranteeing that some long-term constraints are satisfied during the learning process.
Autoregressive Bandits
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing.
Dynamic Pricing with Volume Discounts in Online Settings
We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration.
