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Tuan Anh Le
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Year
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
1952018
Tighter Variational Bounds are Not Necessarily Better
T Rainforth, AR Kosiorek, TA Le, CJ Maddison, M Igl, F Wood, YW Teh
International Conference on Machine Learning, 2018
1642018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1432018
Inference Compilation and Universal Probabilistic Programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics 54†…, 2017
1272017
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
TA Le, AG Baydin, R Zinkov, F Wood
30th International Joint Conference on Neural Networks, 3514--3521, 2017
1032017
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
TA Le, AR Kosiorek, N Siddharth, YW Teh, F Wood
Proc. of the Conf. on Uncertainty in AI (UAI), 2019
46*2019
Bayesian optimization for probabilistic programs
T Rainforth, TA Le, JW van de Meent, MA Osborne, F Wood
Advances In Neural Information Processing Systems, 280-288, 2016
342016
The Thermodynamic Variational Objective
V Masrani, TA Le, F Wood
Advances in Neural Information Processing Systems, 11525-11534, 2019
292019
Empirical Evaluation of Neural Process Objectives
TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh
252018
Learning to learn generative programs with Memoised Wake-Sleep
LB Hewitt, TA Le, JB Tenenbaum
Uncertainty in Artificial Intelligence, 2020
112020
Inference for higher order probabilistic programs
TA Le
Masters thesis, University of Oxford, 2015
72015
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
H Wu, H Zimmermann, E Sennesh, TA Le, JW van de Meent
International Conference on Machine Learning, 2020
52020
Data-driven Sequential Monte Carlo in Probabilistic Programming
YN Perov, TA Le, F Wood
NIPS Workshop on Black Box Learning and Inference, 2015
52015
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
ML Casado, AG Baydin, DM Rubio, TA Le, F Wood, L Heinrich, G Louppe, ...
NIPS Workshop on Deep Learning for Physical Sciences, 2017
42017
Nested Compiled Inference for Hierarchical Reinforcement Learning
TA Le, AG Baydin, F Wood
NIPS Workshop on Bayesian Deep Learning, 2016
42016
Semi-supervised Sequential Generative Models
M Teng, TA Le, A Scibior, F Wood
Uncertainty in Artificial Intelligence, 2020
32020
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model
M Hofer, TA Le, R Levy, J Tenenbaum
arXiv preprint arXiv:2104.08274, 2021
12021
Imitation Learning of Factored Multi-agent Reactive Models
M Teng, TA Le, A Scibior, F Wood
arXiv preprint arXiv:1903.04714, 2019
12019
Amortized inference and model learning for probabilistic programming
TA Le
University of Oxford, 2019
12019
Drawing out of Distribution with Neuro-Symbolic Generative Models
Y Liang, JB Tenenbaum, TA Le, N Siddharth
arXiv preprint arXiv:2206.01829, 2022
2022
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Articles 1–20