2 citations · 2 across the 8 of their papers we have counts for
8 papers
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
Lixing Zhang, Yidong Ouyang, Weifu Li +3
Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many re…
HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
Zewei Deng, Tinghan Ye, Liyan Xie
Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an early ambiguity can affect la…
Training-Free Self-Correction for Multimodal Masked Diffusion Models
Yidong Ouyang, Panwen Hu, Zhengyan Wan +7
Masked diffusion models have emerged as a powerful framework for text and multimodal generation. However, their sampling procedure updates multiple tokens simultaneously and treats…
Alignment of Diffusion Model and Flow Matching for Text-to-Image Generation
Yidong Ouyang, Liyan Xie, Hongyuan Zha +1
Diffusion models and flow matching have demonstrated remarkable success in text-to-image generation. While many existing alignment methods primarily focus on fine-tuning pre-traine…
Corrected Samplers for Discrete Flow Models
Zhengyan Wan, Yidong Ouyang, Liyan Xie +3
Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alternative to discrete diffusion models. A…
Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching
Zhengyan Wan, Yidong Ouyang, Liyan Xie +3
Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When modeling discrete data, existing…
Error Analysis of Discrete Flow with Generator Matching
Zhengyan Wan, Yidong Ouyang, Qiang Yao +4
Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion…
Transfer Learning for Diffusion Models
Yidong Ouyang, Liyan Xie, Hongyuan Zha +1
Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical p…