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Speaker:
Peyman Mohajerin Esfahani

This event is part of an event series „Fachbereichskolloquium“.

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).