5 papers
Diffusion Models for Adaptive Sequential Data Generation
Haoyang Cao, Minshuo Chen, Yinbin Han +1
Generating realistic synthetic sequential data is critical in real-world applications across operations research, finance, healthcare, energy systems, and scientific computing, whe…
CreFlow: Corrective Reflow for Sparse-Reward Embodied Video Diffusion RL
Zhenyang Ni, Yijiang Li, Ruochen Jiao +7
Video generation models trained on heterogeneous data with likelihood-surrogate objectives can produce visually plausible rollouts that violate physical constraints in embodied man…
Training-Free Adaptation of Diffusion Models via Doob's -Transform
Qijie Zhu, Zeqi Ye, Han Liu +2
Adaptation methods have been a workhorse for unlocking the transformative power of pre-trained diffusion models in diverse applications. Existing approaches often abstract adaptati…
Parameter-Efficient Subspace Optimization for LLM Fine-Tuning
Yuchen Lou, Zeqi Ye, Minshuo Chen
This paper develops a new perspective on parameter-efficient fine-tuning (PEFT) for LLMs, inspired by classical subspace minimization. We introduce a unifying framework, Parameter-…
Provable Separations between Memorization and Generalization in Diffusion Models
Zeqi Ye, Qijie Zhu, Molei Tao +1
Diffusion models have achieved remarkable success across diverse domains, but they remain vulnerable to memorization -- reproducing training data rather than generating novel outpu…