J. Blanchet, F. Zhang, Y. Kang and Z. Hu, “A Distributionally Robust Boosting Algorithm,” 2019 Winter Simulation Conference (WSC), National Harbor, MD, USA, 2019, pp. 3728-3739, doi: 10.1109/WSC40007.2019.9004804. keywords: {Boosting;Robustness;Prediction algorithms;Uncertainty;Decision making;Tuning;Atmospheric measurements},

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Abstract

Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be interpreted as the solutions of suitable formulated data-driven DRO problems. In turn, this connection is used to optimally select tuning parameters in terms of a principled approach informed by robustness considerations. This paper contributes to this growing literature, connecting DRO and statistics, by showing how boosting algorithms can be studied via DRO. We propose a boosting type algorithm, named DRO-Boosting, as a procedure to solve our DRO formulation. Our DRO-Boosting algorithm recovers Adaptive Boosting (AdaBoost) in particular, thus showing that AdaBoost is effectively solving a DRO problem. We apply our algorithm to a financial dataset on credit card …

Authors
Jose Blanchet, Fan Zhang, Yang Kang, Zhangyi Hu
Publication date
2019/12/8
Conference
2019 Winter Simulation Conference (WSC)
Pages
3728-3739
Publisher
IEEE