Optimal Transport for Generative Modeling: From Training to Fine-Tuning

• Time : 4:00PM - 6:00PM
  
• Venue : 27-220, Seoul National University
  
• Speaker : 최재웅 (Sungkyunkwan University)
  

Optimal transport (OT) theory provides a principled framework for modeling transformations between probability distributions. From this perspective, generative models can be viewed as mechanisms for transporting a simple prior distribution to a target data distribution. In this talk, we present a unified perspective on generative modeling through OT. We discuss how OT-based frameworks provide principled tools for both training and fine-tuning generative models. First, we introduce Unbalanced Optimal Transport (UOT)-based methods for robust training, long-tailed generation, and unlearning. Second, we discuss Wasserstein Gradient Flow (WGF)-based methods, where generative modeling and reward-guided fine-tuning are formulated as steepest-descent dynamics of functionals in Wasserstein space. Finally, we present Schrödinger Bridge and stochastic optimal control perspectives for efficient path-space generative modeling and reward alignment.