publications

2024

  1. sedd.gif
    Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
    Aaron Lou, Chenlin Meng, and Stefano Ermon
    In Preprint, 2024
  2. ICLR
    Denoising Diffusion Bridge Models
    Linqi Zhou, Aaron Lou, Samar Khanna, and Stefano Ermon
    In International Conference on Learning Representations, 2024

2023

  1. Scaling Riemannian Diffusion Models
    Aaron Lou, Minkai Xu, and Stefano Ermon
    In Neural Information Processing Systems, 2023
  2. Riemannian Residual Neural Networks
    Isay Katsman, Eric Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa
    In Neural Information Processing Systems, 2023
  3. refldiff.gif
    Reflected Diffusion Models
    Aaron Lou, and Stefano Ermon
    In International Conference on Machine Learning, 2023

2021

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    Equivariant Manifold Flows
    Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa
    In Neural Information Processing Systems, 2021
  2. Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
    Tolga Birdal, Aaron Lou, Leonidas J. Guibas, and Umut Simsekli
    In Neural Information Processing Systems, 2021

2020

  1. nmode.gif
    Neural Manifold Ordinary Differential Equations
    Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa
    In Neural Information Processing Systems, 2020
  2. frechet.png
    Differentiating through the Fréchet Mean
    Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, and Christopher De Sa
    In International Conference on Machine Learning, 2020