Tuan Anh Le

Affiliations:
  • Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, Cambridge, MA, USA
  • University of Oxford, UK (PhD 2020)


According to our database1, Tuan Anh Le authored at least 19 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Neural Amortized Inference for Nested Multi-Agent Reasoning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2022
Drawing out of Distribution with Neuro-Symbolic Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model.
CoRR, 2021

2020
Amortized inference and model learning for probabilistic programming.
PhD thesis, 2020

Semi-supervised Sequential Generative Models.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Learning to learn generative programs with Memoised Wake-Sleep.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Amortized Population Gibbs Samplers with Neural Sufficient Statistics.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

The Thermodynamic Variational Objective.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Revisiting Reweighted Wake-Sleep.
CoRR, 2018

Tighter Variational Bounds are Not Necessarily Better.
Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Variational Reinforcement Learning for POMDPs.
Proceedings of the 35th International Conference on Machine Learning, 2018

Auto-Encoding Sequential Monte Carlo.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.
CoRR, 2017

Using synthetic data to train neural networks is model-based reasoning.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Inference Compilation and Universal Probabilistic Programming.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Bayesian Optimization for Probabilistic Programs.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Data-driven Sequential Monte Carlo in Probabilistic Programming.
CoRR, 2015


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