About me

Hi, I am Yilun Xu, a research scientist in NVIDIA Research, fundamental GenAI research team.

📢 🔥 We are hiring research intern working on diffusion acceleration (e.g., one-step distillation)! Please consider reaching out to me if you are interested!

I received my PhD in MIT EECS, advised by Tommi Jaakkola. I obtained my Bachelor’s degree from Turing Class in EECS dept, Peking University. I had the privilege of working with Yizhou Wang in PKU, and Stefano Ermon in Stanford. I also worked with Karsten Kreis and Arash Vahdat in NVIDIA Research, during summer 2023.

My research interests are machine learning, with a current emphasis on generative modeling: (i) new family of generative models [13, 12, 10]; (ii) improving the training, sampling and controllability of generative models, e.g. PFGM and diffusion models [16, 15, 14, 11, 9, 5]. Previously, I worked on bridging machine learning and information theory [4, 3, 2, 1].

Contact: yilunx@nvidia.com, ylxu@mit.edu , xuyilun@pku.edu.cn

Publications

(*) denotes equal contribution

  1. Truncated Consistency Models
    Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, Weili Nie.
    In preprint, 2024
    [PDF] ,[Project Page]
  2. Heavy-Tailed Diffusion Models
    Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael Pritchard, Arash Vahdat, Morteza Mardani.
    In preprint, 2024
    [PDF]
  3. 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 preprint, 2024
    [PDF]
  4. 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 preprint, 2024
    [PDF]
  5. Hamiltonian Score Matching and Generative Flows
    Peter Holderrieth, Yilun Xu, Tommi Jaakkola.
    In Neural Information Processing Systems (NeurIPS), 2024.
    [PDF]
  6. On Physics-Inspired Generative Models
    Yilun Xu.
    PhD Thesis 🎓, Massachusetts Institute of Technology.
    [PDF]
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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],
  12. 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
  13. 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
  14. 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
  15. 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
  16. Learning Representations that Support Robust Transfer of Predictors
    Yilun Xu, Tommi Jaakkola.
    In preprint, 2022
    [PDF] ,[Code]
  17. 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]
  18. 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
  19. 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)],
  20. 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]
  21. 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
  22. 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

Education

    Sep. 2021* - May. 2024: Massachusetts Institute of Technology

     Ph.D. in Computer Science

     Advisor: Tommi Jaakkola

     *: remote during Sept 2020 - Aug 2021

   Sep. 2016 - July. 2020: Peking University

     B.S. in Turing Class, Computer Science (summa cum laude)

     Advisor: Yizhou Wang

   Jun. 2019 - Sep. 2019: Stanford University

     Visiting reseacher

     Advisor: Stefano Ermon


Work Experience

   Jun. 2023 - May, 2024: NVIDIA

     Research Intern

     Advisor: Karsten Kreis and Arash Vahdat


Talks

Service

Conference Reviewer: NeurIPS 2020, 2021, 2022, 2023; ICLR 2021, 2022, 2023, 2024; ICML 2021, 2022, 2023

Workshop Reviewer: ICML-AI4Science 2022

Journal Reviewer: TMLR, TPAMI

PC member: ICLR 2022 PAIR2Struct Workshop

Panelist: ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling

Teaching Assistant for 6.S052/6.S952: Modeling with Machine learning for CS, MIT, Spring 2023 (co-design the problem set and lectures for this new course, and receive an outstanding TA rating of 6.9 out of 7.0 from students)

Miscellaneous

I am a national second-class table tennis player in China. Men’s single champion in PKU Freshman’s Cup (2016). I also played for MIT table tennis team, winning the 3rd place (group) in the 2022 NCTTA Upper New England Championship.

I have been playing PUBG (mobile), LoL a lot. Find me out!