290 citations · 1.3k across the 57 of their papers we have counts for
7 papers · 2 filters
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…
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Guoqiang Wu, Chongxuan Li, Yilong Yin
Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solv…
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
Cheng Lu, Huayu Chen, Jianfei Chen +3
Guided sampling is a vital approach for applying diffusion models in real-world tasks that embeds human-defined guidance during the sampling procedure. This paper considers a gener…
Toward Understanding Generative Data Augmentation
Chenyu Zheng, Guoqiang Wu, Chongxuan Li
Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various lea…
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
Zhengyi Wang, Cheng Lu, Yikai Wang +4
Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-satura…
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Fan Bao, Shen Nie, Kaiwen Xue +7
This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning d…
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Chenyu Zheng, Guoqiang Wu, Fan Bao +3
A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trai…