Orateur
Description
Recent advances in operations research (OR) and machine learning (ML) have spurred interest in integrating prediction algorithms with optimization techniques to address decision-making under uncertainty. This has led to the emergence of contextual optimization, a field focused on data-driven methods that prescribe actions based on the most recently available information. These models appear in both OR and ML literature under various names, including data-driven optimization, prescriptive optimization, predictive stochastic programming, policy optimization, (smart) predict-then-optimize, decision-focused learning, and (task-based) end-to-end learning/forecasting/optimization.
In this talk, we will see that these approaches can be unified under the contextual optimization framework. Then, I will discuss some models and methodologies for learning policies from data and the associated challenges.
