2016
A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning
AISTATS 2016poster
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power. Our pro…