The sample complexity of learning transport maps
- Series
- Stochastics Seminar
- Time
- Thursday, March 30, 2023 - 15:30 for 1 hour (actually 50 minutes)
- Location
- Skiles 006
- Speaker
- Philippe Rigollet – Massachusetts Institute of Technology
Optimal transport has recently found applications in a variety of fields ranging from graphics to biology. Underlying these applications is a new statistical paradigm where the goal is to couple multiple data sources. It gives rise to interesting new questions ranging from the design of estimators to minimax rates of convergence. I will review several applications where the central problem consists in estimating transport maps. After studying optimal transport as a potential solution, I will argue that its entropic version is a good alternative model. In particular, it completely escapes the curse of dimensionality that plagues statistical optimal transport.