A stagewise greedy algorithm for semiparametric contextual dynamic pricing achieves regret T to the max of 1/2 and 3 over (2 beta plus 1) for linear m, with a matching lower bound proving optimality.
Management Science , volume=
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POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
citing papers explorer
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Optimal Semiparametric Dynamic Pricing with Feature Diversity
A stagewise greedy algorithm for semiparametric contextual dynamic pricing achieves regret T to the max of 1/2 and 3 over (2 beta plus 1) for linear m, with a matching lower bound proving optimality.
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Privacy Preserving Reinforcement Learning with One-Sided Feedback
POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.