Title: Robust Data-Driven Approach in Decision Making Under Uncertainty
Speaker: Grani Adiwena Hanasusanto
Affiliation: Department of Computing – Imperial College London
Location: Room 301 William Penney
Time: 4:00pm

Abstract. We investigate robust data-driven approach in stochastic optimization problems where partial knowledge on the exogenous uncertainties is available to the decision maker. In contrast to the traditional model-based approach, a data-driven approach requires no assumptions on the underlying distribution of exogenous uncertainties. Estimation of conditional expectation is achieved using kernel regression scheme which evaluates the cost function solely at historical observations. If sparse historical observations are available, however, the estimation is inaccurate and the resulting decision performs poorly in out-of-sample tests. To alleviate this unfavourable outcome, we ‘robustify’ the decision against estimation errors by utilizing techniques from robust optimization. We show that the arising min-max problem can be reformulated as a tractable conic program. We further extend the proposed approach to multi-period settings and introduce an approximate dynamic programming framework that retains the tractability of the formulation and that is amenable to efficient parallel implementation. The proposed approach is tested across several application domains and is shown to outperform various non-robust schemes in terms of standard statistical benchmarks.

About the speaker. Grani Hanasusanto is a PhD student at the Department of Computing, Imperial College London, under the supervision of Dr. Daniel Kuhn. He obtained the BEng (Hons) degree in Electrical and Electronic Engineering from Nanyang Technological University, Singapore, and the MSc degree in Financial Engineering from National University of Singapore. His research interests are in numerical and computational methods and their applications.