Integer optimization for predictive and prescriptive analytics in high stakes domains

Phebe Vayanos
University of Southern California (USC)

Motivated by problems in homeless services delivery, suicide prevention, and substance use prevention, we consider the problem of learning optimal interpretable, robust, and fair models in the form of decision-trees to assist with decision-making in high-stakes settings. We propose new discrete optimization models and algorithms, showcase their flexibility, and theoretical and practical benefits, and demonstrate substantial improvements over the state of the art. This presentation is based on the following papers:
Strong optimal classification trees, S. Aghaei, A. Gómez, P. Vayanos. Major revision at Operations Research.
Learning optimal fair classification trees, N. Jo, S. Aghaei, J. Benson, A. Gómez, P. Vayanos. Working Paper, 2022.
Learning optimal prescriptive trees from observational data, N. Jo, S. Aghaei, A. Gómez, P. Vayanos, Major revision at Management Science.
Optimal robust classification trees, N. Justin, S. Aghaei, A. Gómez, P. Vayanos, AAAI Workshop on Adversarial Machine Learning and Beyond, 2022

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