Data-driven Sequential Monte Carlo in Probabilistic Programming. - Yura N. Perov; Tuan Anh Le 001; Frank D. Wood (CoRR, 2015)

Bayesian Optimization for Probabilistic Programs. - Tom Rainforth; Tuan Anh Le 001; Jan-Willem van de Meent; Michael A. Osborne; Frank D. Wood (NIPS, 2016)

Inference Compilation and Universal Probabilistic Programming. - Tuan Anh Le 001; Atilim Gunes Baydin; Frank D. Wood (AISTATS, 2017)

Using synthetic data to train neural networks is model-based reasoning. - Tuan Anh Le 001; Atilim Günes Baydin; Robert Zinkov; Frank D. Wood (IJCNN, 2017)

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. - Mario Lezcano Casado; Atilim Gunes Baydin; David Martínez-Rubio; Tuan Anh Le 001; Frank D. Wood; Lukas Heinrich; Gilles Louppe; Kyle Cranmer; Karen Ng; Wahid Bhimji; Prabhat (CoRR, 2017)

Auto-Encoding Sequential Monte Carlo. - Tuan Anh Le 001; Maximilian Igl; Tom Rainforth; Tom Jin; Frank Wood (ICLR (Poster), 2018)

Deep Variational Reinforcement Learning for POMDPs. - Maximilian Igl; Luisa M. Zintgraf; Tuan Anh Le 001; Frank Wood; Shimon Whiteson (ICML, 2018)

Tighter Variational Bounds are Not Necessarily Better. - Tom Rainforth; Adam R. Kosiorek; Tuan Anh Le 001; Chris J. Maddison; Maximilian Igl; Frank Wood; Yee Whye Teh (ICML, 2018)

Revisiting Reweighted Wake-Sleep. - Tuan Anh Le 001; Adam R. Kosiorek; N. Siddharth 001; Yee Whye Teh; Frank Wood (CoRR, 2018)

The Thermodynamic Variational Objective. - Vaden Masrani; Tuan Anh Le 001; Frank Wood (NeurIPS, 2019)

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. - Tuan Anh Le 001; Adam R. Kosiorek; N. Siddharth 001; Yee Whye Teh; Frank Wood (UAI, 2019)

Amortized Population Gibbs Samplers with Neural Sufficient Statistics. - Hao Wu 020; Heiko Zimmermann; Eli Sennesh; Tuan Anh Le 001; Jan-Willem van de Meent (ICML, 2020)

Learning to learn generative programs with Memoised Wake-Sleep. - Luke B. Hewitt; Tuan Anh Le 001; Joshua B. Tenenbaum (UAI, 2020)

Semi-supervised Sequential Generative Models. - Michael Teng; Tuan Anh Le 001; Adam Scibior; Frank Wood (UAI, 2020)

Amortized inference and model learning for probabilistic programming. - Tuan Anh Le 001 (2020)

Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model. - Matthias Hofer 002; Tuan Anh Le 001; Roger Levy; Josh Tenenbaum 001 (CogSci, 2021)

Explaining the Gestalt principle of common fate as amortized inference. - Yoni Friedman; Tuan Anh Le 001; Bernhard Egger 001; Max H. Siegel; Josh Tenenbaum 001 (CogSci, 2021)

Benchmarking mid-level vision with texture-defined 3D objects. - Yoni Friedman; Thomas P. O'Connell; Max H. Siegel; Daniel M. Bear; Tuan Anh Le 001; Bernhard Egger 001; Josh Tenenbaum 001 (CogSci, 2022)

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. - Tuan Anh Le 001; Katherine M. Collins; Luke Hewitt; Kevin Ellis; Siddharth Narayanaswamy; Samuel Gershman; Joshua B. Tenenbaum (ICLR, 2022)

Drawing out of Distribution with Neuro-Symbolic Generative Models. - Yichao Liang; Josh Tenenbaum 001; Tuan Anh Le 001; N. Siddharth 001 (NeurIPS, 2022)

Neural Amortized Inference for Nested Multi-Agent Reasoning. - Kunal Jha; Tuan Anh Le 001; Chuanyang Jin; Yen-Ling Kuo; Joshua B. Tenenbaum; Tianmin Shu (AAAI, 2024)

Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning. - Top Piriyakulkij; Cassidy Langenfeld; Tuan Anh Le 001; Kevin Ellis (NeurIPS, 2024)