ORBIT learns the (β-1)-smooth oracle price map via local polynomial approximation and bandit convex optimization in a semiparametric contextual pricing model, achieving regret Õ(T^{(2β-1)/(4β-3)} + √(dT)) with a matching lower bound for fixed d.
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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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Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
ORBIT learns the (β-1)-smooth oracle price map via local polynomial approximation and bandit convex optimization in a semiparametric contextual pricing model, achieving regret Õ(T^{(2β-1)/(4β-3)} + √(dT)) with a matching lower bound for fixed d.
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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.