Tuan Anh Le

Conference proceedings

  1. Le, T. A., Collins, K. M., Hewitt, L., Ellis, K., N, S., Gershman, S., & Tenenbaum, J. B. (2022). Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. International Conference on Learning Representations.
    @inproceedings{le2022hybrid,
      title = {Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface},
      author = {Le, Tuan Anh and Collins, Katherine M. and Hewitt, Luke and Ellis, Kevin and N, Siddharth and Gershman, Samuel and Tenenbaum, Joshua B.},
      booktitle = {International Conference on Learning Representations},
      year = {2022},
      arxiv = {https://arxiv.org/abs/2107.06393},
      code = {https://github.com/tuananhle7/hmws}
    }
    
  2. Hewitt, L. B., Le, T. A., & Tenenbaum, J. B. (2020). Learning to learn generative programs with Memoised Wake-Sleep. Uncertainty in Artificial Intelligence.
    @inproceedings{hewitt2020learning,
      title = {Learning to learn generative programs with Memoised Wake-Sleep},
      author = {Hewitt, Luke B and Le, Tuan Anh and Tenenbaum, Joshua B},
      booktitle = {Uncertainty in Artificial Intelligence},
      year = {2020},
      arxiv = {https://arxiv.org/abs/2007.03132},
      code = {https://github.com/tuananhle7/mws}
    }
    
  3. Teng, M., Le, T. A., Scibior, A., & Wood, F. (2020). Semi-supervised Sequential Generative Models. Uncertainty in Artificial Intelligence.
    @inproceedings{teng2020semisupervised,
      title = {Semi-supervised Sequential Generative Models},
      author = {Teng, Michael and Le, Tuan Anh and Scibior, Adam and Wood, Frank},
      booktitle = {Uncertainty in Artificial Intelligence},
      year = {2020},
      arxiv = {https://arxiv.org/abs/2007.00155}
    }
    
  4. Wu, H., Zimmermann, H., Sennesh, E., Le, T. A., & van de Meent, J.-W. (2020). Amortized Population Gibbs Samplers with Neural Sufficient Statistics. International Conference on Machine Learning.
    @inproceedings{wu2020amortized,
      title = {Amortized Population Gibbs Samplers with Neural Sufficient Statistics},
      author = {Wu, Hao and Zimmermann, Heiko and Sennesh, Eli and Le, Tuan Anh and van de Meent, Jan-Willem},
      booktitle = {International Conference on Machine Learning},
      year = {2020},
      arxiv = {https://arxiv.org/abs/1911.01382},
      code = {https://github.com/hao-w/apg-samplers}
    }
    
  5. Masrani, V., Le, T. A., & Wood, F. (2019). The Thermodynamic Variational Objective. Advances in Neural Information Processing Systems, 11525–11534.
    @inproceedings{masrani2019thermodynamic,
      title = {The Thermodynamic Variational Objective},
      author = {Masrani, Vaden and Le, Tuan Anh and Wood, Frank},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {11525--11534},
      year = {2019},
      arxiv = {https://arxiv.org/abs/1907.00031},
      code = {https://github.com/vmasrani/tvo}
    }
    
  6. Le*, T. A., Kosiorek*, A. R., Siddharth, N., Teh, Y. W., & Wood, F. (2019). Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. Uncertainty in Artificial Intelligence.
    @inproceedings{le2019revisiting,
      title = {Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow},
      author = {Le*, Tuan Anh and Kosiorek*, Adam R. and Siddharth, N. and Teh, Yee Whye and Wood, Frank},
      booktitle = {Uncertainty in Artificial Intelligence},
      year = {2019},
      arxiv = {https://arxiv.org/abs/1805.10469},
      code = {https://github.com/tuananhle7/rrws},
      note = {Le and Kosiorek contributed equally.}
    }
    
  7. Igl, M., Zintgraf, L., Le, T. A., Wood, F., & Whiteson, S. (2018). Deep Variational Reinforcement Learning for POMDPs. International Conference on Machine Learning.
    @inproceedings{igl2018deep,
      title = {Deep Variational Reinforcement Learning for POMDPs},
      author = {Igl, Maximilian and Zintgraf, Luisa and Le, Tuan Anh and Wood, Frank and Whiteson, Shimon},
      booktitle = {International Conference on Machine Learning},
      year = {2018},
      arxiv = {https://arxiv.org/abs/1806.02426},
      code = {https://github.com/maximilianigl/DVRL}
    }
    
  8. Rainforth, T., Kosiorek, A. R., Le, T. A., Maddison, C. J., Igl, M., Wood, F., & Teh, Y. W. (2018). Tighter Variational Bounds are Not Necessarily Better. International Conference on Machine Learning.
    @inproceedings{rainforth2018tighter,
      title = {Tighter Variational Bounds are Not Necessarily Better},
      author = {Rainforth, Tom and Kosiorek, Adam R. and Le, Tuan Anh and Maddison, Chris J. and Igl, Maximilian and Wood, Frank and Teh, Yee Whye},
      booktitle = {International Conference on Machine Learning},
      year = {2018},
      arxiv = {https://arxiv.org/abs/1802.04537}
    }
    
  9. Le, T. A., Igl, M., Rainforth, T., Jin, T., & Wood, F. (2018). Auto-Encoding Sequential Monte Carlo. International Conference on Learning Representations.
    @inproceedings{le2018autoencoding,
      title = {Auto-Encoding Sequential {M}onte {C}arlo},
      author = {Le, Tuan Anh and Igl, Maximilian and Rainforth, Tom and Jin, Tom and Wood, Frank},
      booktitle = {International Conference on Learning Representations},
      year = {2018},
      arxiv = {https://arxiv.org/abs/1705.10306},
      code = {https://github.com/tuananhle7/aesmc}
    }
    
  10. Le, T. A., Baydin, A. G., Zinkov, R., & Wood, F. (2017). Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. 30th International Joint Conference on Neural Networks, 3514–3521.
    @inproceedings{le2017synthetic,
      author = {Le, Tuan Anh and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Zinkov, Robert and Wood, Frank},
      booktitle = {30th International Joint Conference on Neural Networks},
      title = {Using Synthetic Data to Train Neural Networks is Model-Based Reasoning},
      pages = {3514--3521},
      address = {Anchorage, AK, USA},
      year = {2017},
      publisher = {IEEE},
      arxiv = {https://arxiv.org/abs/1703.00868}
    }
    
  11. Le, T. A., Baydin, A. G., & Wood, F. (2017). Inference Compilation and Universal Probabilistic Programming. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 1338–1348.
    @inproceedings{le2017inference,
      author = {Le, Tuan Anh and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Wood, Frank},
      booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
      title = {Inference Compilation and Universal Probabilistic Programming},
      year = {2017},
      volume = {54},
      pages = {1338--1348},
      series = {Proceedings of Machine Learning Research},
      address = {Fort Lauderdale, FL, USA},
      publisher = {PMLR},
      code = {https://github.com/pyprob/pyprob},
      arxiv = {https://arxiv.org/abs/1610.09900}
    }
    
  12. Rainforth, T., Le, T. A., van de Meent, J.-W., Osborne, M. A., & Wood, F. (2016). Bayesian Optimization for Probabilistic Programs. Advances in Neural Information Processing Systems, 280–288.
    @inproceedings{rainforth2016bayesian,
      title = {Bayesian {O}ptimization for {P}robabilistic {P}rograms},
      author = {Rainforth, Tom and Le, Tuan Anh and van de Meent, Jan-Willem and Osborne, Michael A and Wood, Frank},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {280--288},
      year = {2016},
      arxiv = {https://arxiv.org/abs/1707.04314},
      video = {https://www.youtube.com/watch?v=gVzV-NxKa9U},
      code = {https://github.com/probprog/bopp}
    }
    

Workshop publications

  1. Le, T. A., Kim, H., Garnelo, M., Rosenbaum, D., Schwarz, J., & Teh, Y. W. (2018). Empirical Evaluation of Neural Process Objectives. NeurIPS Workshop on Bayesian Deep Learning.
    @inproceedings{le2018empirical,
      title = {Empirical Evaluation of Neural Process Objectives},
      author = {Le, Tuan Anh and Kim, Hyunjik and Garnelo, Marta and Rosenbaum, Dan and Schwarz, Jonathan and Teh, Yee Whye},
      booktitle = {NeurIPS Workshop on Bayesian Deep Learning},
      year = {2018},
      file = {http://bayesiandeeplearning.org/2018/papers/92.pdf}
    }
    
  2. Casado, M. L., Baydin, A. G., Rubio David Martı́nez, Le, T. A., Wood, F., Heinrich, L., Louppe, G., Cranmer, K., Bhimji, W., Ng, K., & Prabhat. (2017). Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. NeurIPS Workshop on Deep Learning for Physical Sciences.
    @inproceedings{casado2017improvements,
      title = {Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators},
      author = {Casado, Mario Lezcano and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Rubio, David Mart{\'\i}nez and Le, Tuan Anh and Wood, Frank and Heinrich, Lukas and Louppe, Gilles and Cranmer, Kyle and Bhimji, Wahid and Ng, Karen and Prabhat},
      booktitle = {NeurIPS Workshop on Deep Learning for Physical Sciences},
      year = {2017},
      arxiv = {https://arxiv.org/abs/1712.07901}
    }
    
  3. Rainforth*, T., Le*, T. A., Igl, M., Maddison, C. J., Teh, Y. W., & Wood, F. (2017). Tighter Variational Bounds are Not Necessarily Better [Workshop Version]. NeurIPS Workshop on Bayesian Deep Learning.
    @inproceedings{rainforth2017tighter,
      author = {Rainforth*, Tom and Le*, Tuan Anh and Igl, Maximilian and Maddison, Chris J and Teh, Yee Whye and Wood, Frank},
      booktitle = {NeurIPS Workshop on Bayesian Deep Learning},
      title = {Tighter Variational Bounds are Not Necessarily Better [Workshop Version]},
      year = {2017},
      file = {../assets/pdf/rainforth2017tighter.pdf}
    }
    
  4. Le, T. A., Baydin, A. G., & Wood, F. (2016). Nested Compiled Inference for Hierarchical Reinforcement Learning. NeurIPS Workshop on Bayesian Deep Learning.
    @inproceedings{le2016nested,
      author = {Le, Tuan Anh and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Wood, Frank},
      booktitle = {NeurIPS Workshop on Bayesian Deep Learning},
      title = {Nested Compiled Inference for Hierarchical Reinforcement Learning},
      year = {2016},
      file = {../assets/pdf/le2016nested.pdf}
    }
    
  5. Perov, Y., Le, T. A., & Wood, F. (2015). Data-driven Sequential Monte Carlo in Probabilistic Programming. NeurIPS Workshop on Black Box Learning and Inference.
    @inproceedings{perov2015datadriven,
      author = {Perov, Yura and Le, Tuan Anh and Wood, Frank},
      booktitle = {NeurIPS Workshop on Black Box Learning and Inference},
      title = {Data-driven Sequential {M}onte {C}arlo in Probabilistic Programming},
      year = {2015},
      file = {../assets/pdf/perov2015datadriven.pdf},
      arxiv = {https://arxiv.org/abs/1512.04387}
    }