Tuesday, March 16, 2021 - 3:00pm to 4:00pm
Event Calendar Category
LIDS Seminar Series
Zoom meeting id
949 8517 7458
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Traditionally, statistical learning is focused on either (i) online learning where data is generated online according to some unknown model; or (ii) offline learning where the entire data is available at the beginning of the process. In this talk, we show that combining both approaches can accelerate learning. First, we show how difficult online learning problems can be reduced to well-understood offline regression problems. Second, we show the impact of pre-existing offline data on online learning and characterize conditions under which offline data helps (does not help) improve online learning. We demonstrate the impact of our work in the context of recommendation systems, multiclass classification problems, and dynamic pricing.
David Simchi-Levi is a Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics. In 2020, he was awarded the prestigious INFORMS Impact Prize for playing a leading role in developing and disseminating a new highly impactful paradigm for the identification and mitigation of risks in global supply chains. He is an INFORMS Fellow and MSOM Distinguished Fellow and the recipient of the 2020 INFORMS Koopman Award given to an outstanding publication in military operations research; Ford Motor Company 2015 Engineering Excellence Award; 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; and 2009 INFORMS Revenue Management and Pricing Section Prize.