Papers

Set your course by the stars, not by the lights of every passing ship.

  • COSMOS-NANO
    Distilled version of COSMOS video foundation models. Featured in Jensen Huang's CES / GTC 2025 keynote.
    NVIDIA
  • One-step Diffusion Models with f-Divergence Distribution Matching
    Yilun Xu, Weili Nie, Arash Vahdat.
    In preprint, coming soon
    [PDF] ,[Project Page]
  • Truncated Consistency Models
    Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, Weili Nie.
    In International Conference on Learning Representations (ICLR), 2025
    [PDF] ,[Code] ,[Project Page]
  • Heavy-Tailed Diffusion Models
    Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael Pritchard, Arash Vahdat, Morteza Mardani.
    In International Conference on Learning Representations (ICLR), 2025
    [PDF]
  • Energy-Based Diffusion Language Models for Text Generation
    Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat.
    In International Conference on Learning Representations (ICLR), 2025
    [PDF]
  • Think While You Generate: Discrete Diffusion with Planned Denoising
    Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi Jaakkola, Rafael Gómez-Bombarelli.
    In International Conference on Learning Representations (ICLR), 2025
    [PDF]
  • Hamiltonian Score Matching and Generative Flows
    Peter Holderrieth, Yilun Xu, Tommi Jaakkola.
    In Neural Information Processing Systems (NeurIPS), 2024.
    [PDF]
  • On Physics-Inspired Generative Models
    Yilun Xu.
    PhD Thesis 🎓, Massachusetts Institute of Technology.
    [PDF]
  • DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
    Yilun Xu, Gabriele Corso, Tommi Jaakkola, Arash Vahdat, Karsten Kreis.
    In International Conference on Machine Learning (ICML), 2024.
    [PDF] ,[Project Page]
    PWC PWC
  • Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
    Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola.
    In International Conference on Learning Representations (ICLR), 2024; Deep Inverse Workshop, Neural Information Processing Systems (NeurIPS), 2023 (Oral); Workshop on Diffusion Models, Neural Information Processing Systems (NeurIPS), 2023.
    [PDF] ,[Code]
    Star
  • Restart Sampling for Improving Generative Processes
    Yilun Xu*, Mingyang Deng*, Xiang Cheng*, Yonglong Tian, Ziming Liu, Tommi Jaakkola.
    In Neural Information Processing Systems (NeurIPS), 2023.
    [PDF] ,[Code]
    News Coverage: [MarkTechPost],
    Star
  • PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
    Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola.
    In International Conference on Machine Learning (ICML), 2023.
    [PDF] ,[Code] ,[Slide]
    News Coverage: [MIT News], [Quanta Magazine], [Jiangmen Venture (CN)]
    Star
    PWC
  • GenPhys: From Physical Processes to Generative Models
    Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark.
    In preprint, 2023
    [PDF]
    News Coverage: [MIT News], [Quanta Magazine],
  • Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
    Yilun Xu*, Shangyuan Tong*, Tommi Jaakkola.
    In International Conference on Learning Representations (ICLR), 2023
    [PDF] ,[Code] ,[Slide] ,[Poster]
    News Coverage: [MarkTechPost],
    Star
  • Poisson Flow Generative Models
    Yilun Xu*, Ziming Liu*, Max Tegmark, Tommi Jaakkola.
    In Neural Information Processing Systems (NeurIPS), 2022. (Spotlight)
    [PDF] ,[Code] ,[Slide] ,[Poster]
    News Coverage: [MIT News], [Quanta Magazine], [AssemblyAI Blog], [MarkTechPost], [Synced (CN)], [PaperWeekly (CN)], [QbitAI (CN)],
    Star
  • Controlling Directions Orthogonal to a Classifier
    Yilun Xu, Hao He, Tianxiao Shen, Tommi Jaakkola.
    In International Conference on Learning Representations (ICLR), 2022. (Spotlight)
    [PDF] ,[Code] ,[Slide] ,[Poster]
    Star
  • A Survey on Generative Diffusion Model
    Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li.
    In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023.
    [PDF] ,[Code]
    Star
  • Learning Representations that Support Robust Transfer of Predictors
    Yilun Xu, Tommi Jaakkola.
    In preprint, 2022
    [PDF] ,[Code]
  • Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
    Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville.
    In International Conference on Machine Learning (ICML), 2021. (Long talk)
    [PDF]
  • Anytime Sampling for Autoregressive Models via Ordered Autoencoding
    Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon.
    In International Conference on Learning Representations (ICLR), 2021
    [PDF] ,[Code] ,[Slide] ,[Poster]
    Star
  • A Theory of Usable Information under Computational Constraints
    Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon.
    In International Conference on Learning Representations (ICLR), 2020. (Oral)
    [PDF] ,[Code] ,[Slide] ,[Video]
    News Coverage: [Synced (CN)],
  • TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
    Xinwei Sun*, Yilun Xu*, Peng Cao, Yuqing Kong, Lingjing Hu, Shanghang Zhang, Yizhou Wang.
    In European Conference on Computer Vision (ECCV), 2020. (Oral)
    [PDF] ,[Code]
  • L_{DMI} : A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
    Yilun Xu*, Peng Cao*, Yuqing Kong, Yizhou Wang.
    In Neural Information Processing Systems (NeurIPS), 2019
    [PDF] ,[Code] ,[Slide]
    News Coverage: [Synced (CN)], [PKU-CFCS News (CN)]
    Star
  • Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
    Peng Cao*, Yilun Xu*, Yuqing Kong, Yizhou Wang.
    In International Conference on Learning Representations (ICLR), 2019
    [PDF] ,[Code] ,[Slide]
    News Coverage: [PKU-CFCS News (CN)]
    Star