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Selected Publications (* equal contribution)
For full publication list, please visit my Google Scholar.
Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
Jingyang Ou, Jiaqi Han, Minkai Xu, Shaoxuan Xu, Jianwen Xie, Stefano Ermon, Yi Wu, Chongxuan Li
International Conference on Learning Representations (ICLR), 2026.
Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
Jiaqi Han, Austin Wang, Minkai Xu, Wenda Chu, Meihua Dang, Haotian Ye, Huayu Chen, Yisong Yue, Stefano Ermon
International Conference on Learning Representations (ICLR), 2026.
CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
Jiaqi Han, Haotian Ye, Puheng Li, Minkai Xu, James Zou, Stefano Ermon
International Conference on Computer Vision (ICCV), 2025.
f-PO: Generalized Preference Optimization with f-divergence Minimization
Jiaqi Han*, Mingjian Jiang*, Yuxuan Song, Stefano Ermon, Minkai Xu*
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
Energy-Based Diffusion Language Models for Text Generation
Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat
International Conference on Learning Representations (ICLR), 2025.
SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights
Ling Yang, Zhaochen Yu, Tianjun Zhang, Minkai Xu, Joseph Gonzalez, Bin Cui, Shuicheng Yan
International Conference on Learning Representations (ICLR), 2025.
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E. Gonzalez, Bin Cui
Neural Information Processing Systems (NeurIPS), 2024. Spotlight [2.08%]
TFG: Unified Training-Free Guidance for Diffusion Models
Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, Stefano Ermon
Neural Information Processing Systems (NeurIPS), 2024. Spotlight [2.08%]
An All-atom Protein Generative Model
Alexander Chu,
Jinho Kim,
Lucy Cheng,
Gina El Nesr,
Minkai Xu,
Richard Shuai,
Po-Ssu Huang
Proceedings of the National Academy of Sciences (PNAS), 121.27 (2024): e2311500121.
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
Ling Yang,
Zhaochen Yu,
Chenlin Meng,
Minkai Xu,
Stefano Ermon,
Bin Cui
International Conference on Machine Learning (ICML), 2024.
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Minkai Xu*,
Jiaqi Han*,
Aaron Lou,
Jean Kossaifi,
Arvind Ramanathan,
Kamyar Azizzadenesheli,
Jure Leskovec,
Stefano Ermon,
Anima Anandkumar
International Conference on Machine Learning (ICML), 2024.
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
Minkai Xu,
Lantao Yu,
Yang Song,
Chence Shi,
Stefano Ermon,
Jian Tang
International Conference on Learning Representations (ICLR), 2022. Oral Presentation [54/3391]
Geometric probabilistic models; Markov chains; SE(3)-equivariance; denoising diffusion.
Learning Gradient Fields for Molecular Conformation Generation
Chence Shi*,
Shitong Luo*,
Minkai Xu,
Jian Tang
International Conference on Machine Learning (ICML), 2021. Long Talk [top 3.0%]
Molecular 3D geometry generation; denoising score-matching; SE(3)-equivariance.
Towards Generalized Implementation of Wasserstein Distance in GANs
Minkai Xu,
Zhiming Zhou,
Guansong Lu,
Jian Tang,
Weinan Zhang,
Yong Yu
AAAI Conference on Artificial Intelligence (AAAI), 2021.
Novel Sobolev duality of Wasserstein distances; Generative Adversarial Nets.
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