Papers

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

• 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],
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• Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
  Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola.
  In Deep Inverse Workshop, Neural Information Processing Systems (NeurIPS), 2023 (Oral); Workshop on Diffusion Models, Neural Information Processing Systems (NeurIPS), 2023.
  [PDF] ,[Code]
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• 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)]
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• 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 preprint, 2022
  [PDF] ,[Code]
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• 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