Xu, Yilun

19 publications

ICLR 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

ICLR 2025 Heavy-Tailed Diffusion Models Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael Pritchard, Arash Vahdat, Morteza Mardani

ICLR 2025 Think While You Generate: Discrete Diffusion with Planned Denoising Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stark, Yilun Xu, Tommi Jaakkola, Rafael Gomez-Bombarelli

ICLR 2025 Truncated Consistency Models Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, Weili Nie

ICML 2024 DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents Yilun Xu, Gabriele Corso, Tommi Jaakkola, Arash Vahdat, Karsten Kreis

NeurIPS 2024 Hamiltonian Score Matching and Generative Flows Peter Holderrieth, Yilun Xu, Tommi Jaakkola

ICLR 2024 Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola

ICML 2023 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola

NeurIPSW 2023 Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola

NeurIPS 2023 Restart Sampling for Improving Generative Processes Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola

ICLR 2023 Stable Target Field for Reduced Variance Score Estimation in Diffusion Models Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola

ICLR 2022 Controlling Directions Orthogonal to a Classifier Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola

NeurIPS 2022 Poisson Flow Generative Models Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola

ICLR 2021 Anytime Sampling for Autoregressive Models via Ordered Autoencoding Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

ICML 2021 Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

ICLR 2020 A Theory of Usable Information Under Computational Constraints Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon

ECCV 2020 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

NeurIPS 2019 L_DMI: A Novel Information-Theoretic Loss Function for Training Deep Nets Robust to Label Noise Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang

ICLR 2019 Max-MIG: An Information Theoretic Approach for Joint Learning from Crowds Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang