Publications |

Xuechen Li, Chris J. Maddison, Daniel Tarlow. Learning to Extend Program Graphs to Work-in-Progress Code. 2021.

Patrick Kidger, James Foster, Xuechen Li, Terry Lyons. Efficient and Accurate Gradients for Neural SDEs. 2021.

Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons. Neural SDEs as Infinite-Dimensional GANs. International Conference on Machine Learning (ICML), 2021.

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Shun-ichi Amari, Jimmy Ba, Roger Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu. When Does Preconditioning Help or Hurt Generalization?. International Conference on Learning Representations (ICLR), 2021.

Preprint

Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud. Scalable Gradients for Stochastic Differential Equations. Short version accepted for spotlight presentation at 2nd Symposium on AABI. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

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Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu. Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. Advances in Neural Information Processing Systems (NeurIPS), 2019.

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George Barmpalias, Neng Huang, Andrew Lewis-Pye, Angsheng Li, Xuechen Li, Yicheng Pan, Tim Roughgarden. The idemetric property: when most distances are (almost) the same. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2019.

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Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud. Isolating Sources of Disentanglement in Variational Autoencoders. Advances in Neural Information Processing Systems (NeurIPS), 2018.

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Chris Cremer, Xuechen Li, David Duvenaud. Inference Suboptimality in Variational Autoencoders. International Conference on Machine Learning (ICML), 2018.

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