Lower Confidence Bound Policy for Optimization bounded Regression

Abstract

Joint work with Professor Cheung Wang-Chi. We study the problem where prediction objectives are some parameters in an optimization problem. We purpose a lower confidence bound policy such that the loss of optimization by predicting the parameters wrong has a tight bound. Moreoever, we purpose a tuning techiqnue such that the prediction by out method substantially outperforms than normal regression. We apply this algorithm to knapsack and network follow optmization problem, deriving an average of 2% decrement in expected optimization loss.

Note

Work in preparation, available upon request.