报告题目：Decision-Driven Regularization: A Blended Model for Predict-then-Optimize
报告人：汤勤深 南洋理工大学
报告时间：2022年9月14日(周三) 16:00-17:30
讲座地点：中国科学院大学中关村校区教学楼S406； 腾讯会议ID：337 514 023
内容摘要
In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost, that varies based on observed features. This context is common in many business applications ranging from on-demand delivery and retail operations to portfolio optimization and inventory management. In this paper, we study the predict-then-optimize approach, which first learns how outcomes result from the features, and then selects optimal decisions based on these outcomes. We identify in the literature an ambiguity in the definition of the cost function, arising out of the lack of access to the true outcomes. To address this, we propose a blended predict-then-optimize framework that may lead to biased predictions on the outcome, but tractably incorporates the optimization problem into the prediction stage. This is achieved by a decision-driven regularization. We critically show that there are three perspectives, from which the predict-then-optimize problem may be approached, namely the regularization, the robust optimization, and the regret minimization approaches; and prove that these perspectives are equivalent to or can be naturally approximated to arrive at our proposed model. As a consequence, our framework generalizes models such as SPO+ in Elmachtoub and Grigas (2020) and JERO in Zhu et al. (2021). Building on our framework, we propose hybrid models that we show numerically to outperform SPO+ under low mis-specification.
主讲人简介
汤勤深，本科和硕士就读于华南理工大学，并于2019年获得新加坡国立大学商业分析与运营系博士学位，现就职于新加坡南洋理工大学南洋商学院，担任信息技术和运营管理系助理教授。他目前主要研究兴趣包括数据驱动和目标导向的决策模型、鲁棒和鲁棒性优化，以及他们在运营和供应链管理中的应用。其研究结果发表在Management Science, Manufacturing & Service Operations Management等期刊。
