Publications

# denotes equal contribution.

An up-to-date list is available on Google Scholar.

Preprints

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    Journal & Conference

    2023

    1. ICML
      Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation
      Song Jiaming, Zhang Qinsheng, Yin Hongxu, Mardani Morteza, Liu Ming-Yu, Kautz Jan, Chen Yongxin, and Vahdat Arash
      In International Conference on Machine Learning. 2023
      PDF Website
    2. CVPR
      DiffCollage: Parallel Generation of Large Content with Diffusion Models
      Zhang Qinsheng, Song Jiaming, Huang Xun, Chen Yongxin, and Liu Ming-yu
      In Conference on Computer Vision and Pattern Recognition. 2023
      arXiv PDF Website
    3. ICLR
      gDDIM: Generalized denoising diffusion implicit models
      Zhang Qinsheng, Tao Molei, and Chen Yongxin
      In International Conference on Learning Representations, Spotlight. 2023
      arXiv url Code
    4. ICLR
      Fast Sampling of Diffusion Models with Exponential Integrator
      Zhang Qinsheng, and Chen Yongxin
      In International Conference on Learning Representations. 2023
      arXiv url Code Website

    2022

    1. arxiv
      ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
      Balaji Yogesh, Nah Seungjun, Huang Xun, Vahdat Arash, Song Jiaming, Zhang Qinsheng, Kreis Karsten, Aittala Miika, Aila Timo, Laine Samuli, Catanzaro Bryan, and others
      tech report. 2022
      arXiv Website
    2. Automatica
      An optimal control approach to particle filtering
      Zhang Qinsheng, Taghvaei Amirhossein, and Chen Yongxin
      2022
      arXiv url
    3. IEEE
      An optimal control approach to particle filtering on Lie groups
      Yuan Bo, Zhang Qinsheng, and Chen Yongxin
      IEEE Control Systems Letters. 2022
      url
    4. ICML
      Variational Wasserstein gradient flow
      Fan Jiaojiao, Zhang Qinsheng, Taghvaei Amirhossein, and Chen Yongxin
      In International Conference on Machine Learning. 2022
      arXiv url PDF
    5. IEEE
      Inference with Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach
      Singh Rahul, Haasler Isabel, Zhang Qinsheng, Karlsson Johan, and Chen Yongxin
      IEEE Transactions on Automatic Control. 2022
      arXiv url Code
    6. IEEE
      Filtering for Aggregate Hidden Markov Models with Continuous Observations
      Zhang Qinsheng#, Singh Rahul#, and Chen Yongxin
      IEEE Control Systems Letters. 2022
      arXiv url Code
    7. ICLR
      Path Integral Sampler: a stochastic control approach for sampling
      Zhang Qinsheng, and Chen Yongxin
      In International Conference on Learning Representations. 2022
      arXiv url Code

    2021

    1. NeurIPS
      Diffusion Normalizing Flow
      Zhang Qinsheng, and Chen Yongxin
      In Advances in Neural Information Processing Systems. 2021
      arXiv url Code
    2. Automatica
      Learning Hidden Markov Models from Aggregate Observations
      Singh Rahul, Zhang Qinsheng, and Chen Yongxin
      Automatica. 2021
      arXiv url

    2020

    1. IEEE
      Multi-marginal optimal transport and probabilistic graphical models
      Zhang Qinsheng#, Haasler Isabel#, Singh Rahul#, Karlsson Johan, and Chen Yongxin
      IEEE Transactions on Information Theory. 2020
      arXiv url Code
    2. L4DC
      Improving robustness via risk averse distributional reinforcement learning
      Zhang Qinsheng#, Singh Rahul#, and Chen Yongxin
      In Learning for Dynamics and Control. 2020
      arXiv PDF Code
    3. CDC
      Incremental inference of collective graphical models
      Singh Rahul, Haasler Isabel, Zhang Qinsheng, Karlsson Johan, and Chen Yongxin
      IEEE Control Systems Letters. 2020
      arXiv url Code