Title
Learning To Price Against A Moving Target
Abstract
In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood when values are stationary (fixed or iid). Here we study the problem where the buyer's value is a moving target, i.e., they change over time either by a stochastic process or adversarially with bounded variation. In either case, we provide matching upper and lower bounds on the optimal revenue loss. Since the target is moving, any information learned soon becomes out-dated, which forces the algorithms to keep switching between exploring and exploiting phases.
Year
Venue
DocType
2021
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
Conference
Volume
ISSN
Citations 
139
2640-3498
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
renato paes133136.45
Balu Sivan200.34
yifeng teng384.04
Pratik Worah4518.90