Publications

Filter by type:

. Large Language Models Can Be Strong Differentially Private Learners. Oral presentation at NeurIPS Privacy in Machine Learning Workshop (PriML’21). Oral presentation at International Conference on Learning Representations (ICLR), 2022.

PDF Code Blog

. Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

PDF

. On the Opportunities and Risks of Foundation Models. 2021.

PDF

. Efficient and Accurate Gradients for Neural SDEs. Advances in Neural Information Processing Systems (NeurIPS), 2021.

PDF

. Neural SDEs as Infinite-Dimensional GANs. International Conference on Machine Learning (ICML), 2021.

PDF

. When Does Preconditioning Help or Hurt Generalization?. Best Student Paper at the 12th OPT Workshop on Optimization for Machine Learning. International Conference on Learning Representations (ICLR), 2021.

Preprint

. Scalable Gradients for Stochastic Differential Equations. Spotlight presentation at the 2nd Symposium on AABI. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

PDF Code Video Slides

. Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. Spotlight presentation at Advances in Neural Information Processing Systems (NeurIPS), 2019.

PDF slides

. The idemetric property: when most distances are (almost) the same. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2019.

PDF

. Isolating Sources of Disentanglement in Variational Autoencoders. Oral presentation at Advances in Neural Information Processing Systems (NeurIPS), 2018.

PDF video

. Inference Suboptimality in Variational Autoencoders. International Conference on Machine Learning (ICML), 2018.

PDF