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Tuan Anh Le
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Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
2422018
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
1892018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1602018
Inference Compilation and Universal Probabilistic Programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics 54 …, 2017
1462017
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
1202017
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
58*2019
The Thermodynamic Variational Objective
V Masrani, TA Le, F Wood
Advances in Neural Information Processing Systems, 11525-11534, 2019
352019
Empirical Evaluation of Neural Process Objectives
TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh
352018
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
352016
Learning to learn generative programs with Memoised Wake-Sleep
LB Hewitt, TA Le, JB Tenenbaum
Uncertainty in Artificial Intelligence, 2020
142020
Inference for higher order probabilistic programs
TA Le
Masters thesis, University of Oxford, 2015
82015
Semi-supervised Sequential Generative Models
M Teng, TA Le, A Scibior, F Wood
Uncertainty in Artificial Intelligence, 2020
52020
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
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
MD Hoffman, TA Le, P Sountsov, C Suter, B Lee, VK Mansinghka, ...
International Conference on Artificial Intelligence and Statistics, 10425-10444, 2023
22023
Drawing out of Distribution with Neuro-Symbolic Generative Models
Y Liang, JB Tenenbaum, TA Le, N Siddharth
Advances in Neural Information Processing Systems, 2022
22022
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
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