About Me

I’m a final year DPhil(PhD) student in the Department of Computer Science at University of Oxford. My current research interest lies in Probabilistic Programming and Machine Learning, especially in optimizing the design of Probabilsitic Programming Languages (PPLs) and automating efficient inferece algorithms in Probabilsitic Programming Systems (PPSs).

I am supervised by Hongseok Yang, Tom Rainforth, Yee Whye Teh, Sam Staton, and Frank Wood , and I am part of Oxford Statistical Machine Learning Group. I am also one of the student representatives in CoGS in CS.

 

Publications

2020

• T. David, Y. Zhou, and H. Yang. “Stochastically Differentiable Probabilistic Programs.” arXiv preprint arXiv:2003.00704 (2020). paper

2019

• Y. Zhou, H. Yang, Y. W. Teh and T. Rainforth, “Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support,” International Conference on Machine Learning (ICML, to appear), 2020.
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• Y. Zhou, B. Gram-Hansen, T. Kohn, T. Rainforth, H. Yang, and F. Wood, “A Low-Level Probabilistic Programming Language for Non-Differentiable Models,” International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
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2018

• Y. Zhou, B. Gram-Hansen, T. Kohn, T. Rainforth, H. Yang, and F. Wood, “Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities,” International Conference of Probabilistic Programming (PORGPROB), 2018.
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• T. Rainforth, Y. Zhou, X. Lu, Y. W. Teh, F. Wood, H. Yang, and J.-W. van de Meent, “Inference Trees: Adaptive Inference with Exploration,” arXiv preprint arXiv:1806.09550, 2018.
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• X. Lu, T. Rainforth, Y. Zhou, J.-W. van de Meent, and Y. W. Teh, “On Exploration, Exploitation and Learning in Adaptive Importance Sampling,” arXiv preprint arXiv:1810.13296, 2018.
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2017

• T. Rainforth, Y. Zhou, X. Lu, Y. W. Teh, F. Wood, H. Yang, and J.-W. van de Meent, “Inference Trees: Adaptive Inference with Exploration [Workshop Version],” NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
  paper

• X. Lu, T. Rainforth, Y. Zhou, J.-W. van de Meent, and Y. W. Teh, “On Exploration, Exploitation and Learning in Adaptive Importance Sampling [Workshop Version],” NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
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(* equal contribution)