Tuan Anh Le
Title
Cited by
Cited by
Year
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
912018
Inference Compilation and Universal Probabilistic Programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics 54 …, 2017
902017
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
802018
Deep Variational Reinforcement Learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
arXiv preprint arXiv:1806.02426, 2018
672018
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
532017
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
302016
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
27*2019
Empirical Evaluation of Neural Process Objectives
TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh
82018
Inference for higher order probabilistic programs
TA Le
Masters thesis, University of Oxford, 2015
62015
The Thermodynamic Variational Objective
V Masrani, TA Le, F Wood
Advances in Neural Information Processing Systems, 11525-11534, 2019
52019
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
arXiv preprint arXiv:2007.00155, 2020
2020
Learning to learn generative programs with Memoised Wake-Sleep
LB Hewitt, TA Le, JB Tenenbaum
Uncertainty in Artificial Intelligence, 2020
2020
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
H Wu, H Zimmermann, E Sennesh, TA Le, JW van de Meent
arXiv preprint arXiv:1911.01382, 2019
2019
Imitation Learning of Factored Multi-agent Reactive Models
M Teng, TA Le, A Scibior, F Wood
arXiv preprint arXiv:1903.04714, 2019
2019
Amortized inference and model learning for probabilistic programming
TA Le
University of Oxford, 2019
2019
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
M Lezcano Casado, A Gunes Baydin, D Martinez Rubio, TA Le, F Wood, ...
2017
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