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Abstract:
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Dynamic pricing is a fast-moving research area in machine learning and operations management. A lot of work has been done for this problem with known noise. We consider a contextual dynamic pricing problem under a linear customer valuation model with an unknown market noise distribution F. This problem is very challenging due to the difficulty in balancing three tangled tasks of revenue-maximization, estimating the linear valuation parameter, and learning the nonparametric F. In this talk, I will discuss a distribution-free pricing policy that learns both the contextual function and the market noise simultaneously. A key ingredient of this method is a novel perturbed linear bandit framework, where a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing.
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