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ROW by Dr. Peyman Mohajerin Esfahani
Time: Jan 24, 2025 at 15:00 CET
Title: Inverse Optimization: The Role of Convexity in Learning
Abstract: In this talk, we first briefly review a general class of data-driven decision-making and highlight the role of convexity in addressing three research questions that arise in this context. We then focus on a particular setting of such problems known as inverse optimization (IO), where the goal is to replicate the behavior of a decision-maker with an unknown objective function. We discuss recent developments in IO concerning convex training losses and optimization algorithms. The main message of this talk is that IO is a rich supervised learning model that can capture a complex (e.g., discontinuous) behavior while the training phase is still a convex program. We motivate the discussion with applications from control (learning the MPC control law), transportation (the 2021 Amazon Routing Problem Challenge), and robotics (comparison with the state-of-the-art in the MuJoCo environments).
Bio: Peyman Mohajerin Esfahani is an Associate Professor at the Delft Center for Systems and Control. He joined TU Delft in October 2016, and prior to that, he held several research appointments at EPFL, ETH Zurich, and MIT between 2014 and 2016. He received the BSc and MSc degrees from Sharif University of Technology, Iran, and the PhD degree from ETH Zurich. He currently serves as an associate editor of Mathematical Programming, Operations Research, Transactions on Automatic Control, and Open Journal of Mathematical Optimization. He was one of the three finalists for the Young Researcher Prize in Continuous Optimization awarded by the Mathematical Optimization Society in 2016, and a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society. He received the ERC Starting Grant and the INFORMS Frederick W. Lanchester Prize in 2020. He is the recipient of the 2022 European Control Award.