Session: AI Meets Optimization (Part 1)
Chair: Wotao Yin
Cluster: Optimization for Emerging Technologies (LLMs, Quantum Computing, ...)
Talk 1: TBD
Speaker: Bartolomeo Stellato
Abstract: TBD
Talk 2: Differentiating through Solutions to Optimization Problems in Decision-Focused Learning
Speaker: Howard Heaton
Abstract: Many real-world problems can be framed as optimization problems, for which well-established algorithms exist. However, these problems often involve key parameters that are not directly observed. Instead, we typically have access to data that is correlated with these parameters, though the relationships are complex and difficult to describe explicitly. This challenge motivates the integration of machine learning with optimization: using machine learning to predict the hidden parameters and optimization to solve the resultant problem. This integration is known as decision-focused learning. In this talk, I will introduce decision-focused learning, with a particular focus on differentiating through solutions to optimization problems and recent advances in effectively scaling these computations.
Talk 3: TBD
Speaker: Ferdinando Fioretto
Abstract: TBD
