Papers

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

  • Truncated Consistency Models
    Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, Weili Nie.
    In preprint, 2024
    [PDF] ,[Project Page]
  • Heavy-Tailed Diffusion Models
    Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael Pritchard, Arash Vahdat, Morteza Mardani.
    In preprint, 2024
    [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 preprint, 2024
    [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 preprint, 2024
    [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