LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models Y Zhou, BJ Gram-Hansen, T Kohn, T Rainforth, H Yang, F Wood The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 34* | 2019 |
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support Y Zhou, H Yang, YW Teh, T Rainforth International Conference on Machine Learning, 11534-11545, 2020 | 21 | 2020 |
Inference trees: Adaptive inference with exploration T Rainforth, Y Zhou, X Lu, YW Teh, F Wood, H Yang, JW van de Meent arXiv preprint arXiv:1806.09550, 2018 | 16 | 2018 |
On exploration, exploitation and learning in adaptive importance sampling X Lu, T Rainforth, Y Zhou, JW van de Meent, YW Teh arXiv preprint arXiv:1810.13296, 2018 | 10 | 2018 |
Symbolic parallel adaptive importance sampling for probabilistic program analysis Y Luo, A Filieri, Y Zhou Proceedings of the 29th ACM Joint Meeting on European Software Engineering …, 2021 | 7* | 2021 |
Probabilistic programs with stochastic conditioning D Tolpin, Y Zhou, T Rainforth, H Yang International Conference on Machine Learning, 10312-10323, 2021 | 6 | 2021 |
Bayesian Policy Search for Stochastic Domains D Tolpin, Y Zhou, H Yang arXiv preprint arXiv:2010.00284, 2020 | 1 | 2020 |
Automating inference for non–standard models Y Zhou University of Oxford, 2020 | | 2020 |