2022/01/27 by Jianyu Xu, Yu-Xiang Wang, Xu, Jianyu +1 · 2 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.2201.11341
openalex publication_date 2022/01/27 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products (described by feature vectors) on the fly by learning from the binary outcomes of previous sales sessions ("Sold" if valuation ≥ price, and "Not Sold" otherwise). Existing works either assume noiseless linear valuation or precisely-known noise distribution, which limits the applicability of those algorithms in practice when these assumptions are hard to verify. In this work, we study two more agnostic models: (a) a "linear policy" problem where we aim at competing with the best linear pricing policy while making no assumptions on the data, and (b) a "linear noisy valuation" problem where the random valuation is linear plus an unknown and assumption-free noise. For the former model, we show a Θ(d\frac13T\frac23) minimax regret up to logarithmic factors. For the latter model, we present an algorithm that achieves an O(T\frac34) regret, and improve the best-known lower bound from Ω(T\frac35) to Ω(T\frac23). These results demonstrate that no-regret learning is possible for feature-based dynamic pricing under weak assumptions, but also reveal a disappointing fact that the seemingly richer pricing feedback is not significantly more useful than the bandit-feedback in regret reduction.