98 citations · 110 across the 15 of their papers we have counts for
19 papers
Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data
Lan Tao, Yongxian He, Shirong Xu +2
Synthetic tabular generators are commonly optimized for distributional fidelity, but statistical similarity alone does not guarantee preservation of causal effects. In this paper,…
Acceleration of Diffusion Language Model through Discrete Average Generator
Yidong Ouyang, Zhengyan Wan, Themis Haris +8
Discrete diffusion models and flow matching have emerged as powerful frameworks for generative modeling over discrete state spaces, yet efficient few-step generation remains a fund…
Iterative Exact Discrete Guidance for Energy-Based Sampling
Yuwen Qian, Yidong Ouyang, Zhengyan Wan +1
Sampling from unnormalized distributions over large discrete state spaces becomes difficult when a multimodal target is far from a tractable reference. We introduce Iterative Exact…
Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow
Tung Sum Thomas Kwok, Yidong Ouyang, Yingjia Wan +3
Uniform discrete flow permits repeated updates at every generation position. While continued revision supports correction of wrong tokens, it also exposes correct intermediate pred…
Reinforcing the Generation Order of Multimodal Masked Diffusion Models
Yidong Ouyang, Zhe Wang, Sourav Bhabesh +1
Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation order…
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…
dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models
Zhengyan Wan, Yidong Ouyang, Panwen Hu +1
Discrete flow models (DFMs) are a class of flexible generative models for generating discrete data, and diffusion large language models (dLLMs) can be viewed as a special case with…
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…
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
Yidong Ouyang, Liyan Xie, Chongxuan Li +1
The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data. However, the vanilla diffusion model requires complete or fully observed…
Improving Adversarial Robustness by Contrastive Guided Diffusion Process
Yidong Ouyang, Liyan Xie, Guang Cheng
Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amoun…
Attention Enables Zero Approximation Error
Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou +1
Deep learning models have been widely applied in various aspects of daily life. Many variant models based on deep learning structures have achieved even better performances. Attent…
Generalizing to Unseen Domains: A Survey on Domain Generalization
Jindong Wang, Cuiling Lan, Chang Liu +6
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unsee…
Robust Learning with Frequency Domain Regularization
Weiyu Guo, Yidong Ouyang
Convolution neural networks have achieved remarkable performance in many tasks of computing vision. However, CNN tends to bias to low frequency components. They prioritize capturin…
Learning Efficient Convolutional Networks through Irregular Convolutional Kernels
Weiyu Guo, Jiabin Ma, Liang Wang +1
As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing…