2018/03/05 by Debjyoti Saharoy, Saharoy, Debjyoti, Theja Tulabandhula +1
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.1803.01968
openalex publication_date 2018/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new efficient online algorithm to learn the parameters governing\nthe purchasing behavior of a utility maximizing buyer, who responds to prices,\nin a repeated interaction setting. The key feature of our algorithm is that it\ncan learn even non-linear buyer utility while working with arbitrary price\nconstraints that the seller may impose. This overcomes a major shortcoming of\nprevious approaches, which use unrealistic prices to learn these parameters\nmaking them unsuitable in practice.\n