Tuan Anh Le
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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
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
Inference Compilation and Universal Probabilistic Programming
TA Le, AG Baydin, F Wood
20th International Conference on Artificial Intelligence and Statistics 54†…, 2017
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
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
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
Empirical Evaluation of Neural Process Objectives
TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh
The Thermodynamic Variational Objective
V Masrani, TA Le, F Wood
Advances in Neural Information Processing Systems, 11525-11534, 2019
Learning to learn generative programs with Memoised Wake-Sleep
LB Hewitt, TA Le, JB Tenenbaum
Uncertainty in Artificial Intelligence, 2020
Data-driven Sequential Monte Carlo in Probabilistic Programming
YN Perov, TA Le, F Wood
NIPS Workshop on Black Box Learning and Inference, 2015
Inference for higher order probabilistic programs
Masters thesis, University of Oxford, 2015
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
Nested Compiled Inference for Hierarchical Reinforcement Learning
TA Le, AG Baydin, F Wood
NIPS Workshop on Bayesian Deep Learning, 2016
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
Semi-supervised Sequential Generative Models
M Teng, TA Le, A Scibior, F Wood
arXiv preprint arXiv:2007.00155, 2020
Imitation Learning of Factored Multi-agent Reactive Models
M Teng, TA Le, A Scibior, F Wood
arXiv preprint arXiv:1903.04714, 2019
Bayesian Optimization for Probabilistic Programs
T Rainforth, TA Le, JW van de Meent, MA Osborne, F Wood
arXiv preprint arXiv:1707.04314, 2017
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
TA Le, KM Collins, L Hewitt, K Ellis, SJ Gershman, JB Tenenbaum
arXiv preprint arXiv:2107.06393, 2021
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model
M Hofer, TA Le, R Levy, J Tenenbaum
arXiv preprint arXiv:2104.08274, 2021
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