Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic
Programs with Stochastic Support

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support

29 October 2019

Papers citing "Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support"

11 / 11 papers shown

Title
Divide, Conquer, Combine Bayesian Decision Tree Sampling Jodie A. Cochrane Adrian G. Wills Sarah J. Johnson 27 1 0 26 Mar 2024
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support Tim Reichelt C. Ong Tom Rainforth 22 2 0 01 Nov 2023
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support Tim Reichelt C.-H. Luke Ong Tom Rainforth 25 0 0 23 Oct 2023
Nonparametric Involutive Markov Chain Monte Carlo Carol Mak Fabian Zaiser C. Ong 20 1 0 02 Nov 2022
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference Wonyeol Lee Xavier Rival Hongseok Yang 16 9 0 22 Aug 2022
Tensor Program Optimization with Probabilistic Programs Junru Shao Xiyou Zhou Siyuan Feng Bohan Hou Ruihang Lai Hongyi Jin Wuwei Lin Masahiro Masuda Cody Hao Yu Tianqi Chen 25 28 0 26 May 2022
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs Yunyao Cheng T. Millstein Guy Van den Broeck Steven Holtzen UQCV TPM 14 3 0 19 Oct 2021
Nonparametric Hamiltonian Monte Carlo Carol Mak Fabian Zaiser C.-H. Luke Ong 23 6 0 18 Jun 2021
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs I. Hasuo Yuichiro Oyabu Clovis Eberhart Kohei Suenaga Kenta Cho Shin-ya Katsumata TPM 22 3 0 05 Jan 2021
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Yicheng Luo A. Filieri Yuanshuo Zhou 11 5 0 10 Oct 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 11 0 0 02 Mar 2020