题　目：Fast rates for contextual linear optimization

嘉　宾：毛小介，助理教授，清华大学经济管理学院

主持人：孙海龙 助理教授 欧宝app官方网站下载安泰经济与管理学院

时　间：2023年3月3日（周五）15:00-16:30pm

地　点：包图B207

内容简介:

Incorporating side observations in decision-making can reduce uncertainty and boost performance, but it also requires us to tackle a potentially complex predictive relationship. While one may use off-the-shelf machine learning methods to separately learn a predictive model and plug it in, a variety of recent methods instead integrate estimation and optimization by fitting the model to directly optimize downstream decision performance. Surprisingly, in the case of contextual linear optimization, we show that the naive plug-in approach actually achieves regret convergence rates that are significantly faster than methods that directly optimize downstream decision performance. While there are other pros and cons to consider as we discuss and illustrate numerically, our results highlight a nuanced landscape for the enterprise to integrate estimation and optimization. Our results are overall positive for practice: predictive models are easy and fast to train using existing tools, simple to interpret, and, as we show, lead to decisions that perform very well.

演讲人简介：

毛小介，清华大学经管学院管理科学与工程系助理教授。2016年获武汉大学数理经济与数理金融系学士学位，2021年获得美国康奈尔大学统计与数据科学博士学位。主要研究方向为因果推断、数据驱动的决策理论与方法，研究涉及统计学、 运筹学、机器学习等多个领域。相关研究成果发表于Operations Research、Management Science、Conference on Neural Information Processing Systems (NeurIPS)、International Conference on Machine Learning (ICML)、International Conference on Artificial Intelligence and Statistics (AISTATS)等国际知名学术期刊和学术会议。

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