3 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Weak-to-Strong Learning in Decision Making
Jingwei Ji, Renyuan Xu
Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental d…
One-Step Generative Modeling via Wasserstein Gradient Flows
Jiaqi Han, Puheng Li, Qiushan Guo +3
Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it requires many iterative updat…
Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes
Hanqing Jin, Renyuan Xu, Yanzhao Yang
We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomially growing rewards: settings th…
Schrödinger bridge for generative AI: Soft-constrained formulation and convergence analysis
Jin Ma, Ying Tan, Renyuan Xu
Generative AI can be framed as the problem of learning a model that maps simple reference measures into complex data distributions, and it has recently found a strong connection to…
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
Yinbin Han, Meisam Razaviyayn, Renyuan Xu
Diffusion models have emerged as powerful tools for generative modeling, demonstrating exceptional capability in capturing target data distributions from large datasets. However, f…
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Yinbin Han, Meisam Razaviyayn, Renyuan Xu
Diffusion models have become a leading paradigm in generative AI, with score estimation via denoising score matching as a central component. While recent theory provides strong sta…