Publications

2023

1. Arxiv

   Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows

   Mingxuan Yi, and Song Liu

   arXiv preprint arXiv:2310.20090, 2023

   PDF
2. Arxiv

   Variational Gradient Descent Using Local Linear Models

   Song Liu, Jack Simons, Mingxuan Yi, and Mark Beaumont

   arXiv preprint arXiv:2305.15577, 2023

   PDF
3. ICML

   MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows

   Mingxuan Yi, Zhanxing Zhu, and Song Liu

   In the 40th International Conference on Machine Learning, 2023

   PDF

2022

1. ACMLAABI

   Sliced Wasserstein Variational Inference

   Mingxuan Yi, and Song Liu

   In the 14th Asian Conference on Machine Learning, also in the 4th Symposium on Advances in Approximate Bayesian Inference (AABI), 2022

   Best student paper (ACML), contributed talk (AABI)

   PDF

2020

1. Arxiv

   Posterior Ratio Estimation of Latent Variables

   Song Liu, Yulong Zhang, Mingxuan Yi, and Mladen Kolar

   arXiv preprint arXiv:2002.06410, 2020

   PDF