Publications

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. When Does Differentially Private Learning Not Suffer in High Dimensions?. Advances in Neural Information Processing Systems (NeurIPS), 2022.

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. 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.

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

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

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

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

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. 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.

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

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

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

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

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