2016/11/02 by Ilan Lobel, Renato Paes Leme, Lobel, Ilan +3 · 4 citations
Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Optimization and Search Problems #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1611.00829
We consider a multidimensional search problem that is motivated by questions in contextual decision-making, such as dynamic pricing and personalized medicine. Nature selects a state from a d-dimensional unit ball and then generates a sequence of d-dimensional directions. We are given access to the directions, but not access to the state. After receiving a direction, we have to guess the value of the dot product between the state and the direction. Our goal is to minimize the number of times when our guess is more than ε away from the true answer. We construct a polynomial time algorithm that we call Projected Volume achieving regret O(dlog(d/ε)), which is optimal up to a log d factor. The algorithm combines a volume cutting strategy with a new geometric technique that we call cylindrification.