Tuan Anh Le

Preprints

  1. Le, T. A., Kosiorek, A. R., Siddharth, N., Teh, Y. W., & Wood, F. (2018). Revisiting Reweighted Wake-Sleep. ArXiv Preprint 1802.04537.
    @article{le2018revisiting,
      title = {Revisiting Reweighted Wake-Sleep},
      author = {Le, Tuan Anh and Kosiorek, Adam R. and Siddharth, N. and Teh, Yee Whye and Wood, Frank},
      journal = {arXiv preprint 1802.04537},
      year = {2018},
      arxiv = {https://arxiv.org/abs/1805.10469},
      note = {Le and Kosiorek contributed equally.}
    }
    

Conference proceedings

  1. Igl, M., Zintgraf, L., Le, T. A., Wood, F., & Whiteson, S. (2018). Deep Variational Reinforcement Learning for POMDPs. In 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},
      file = {../assets/pdf/igl2018deep.pdf},
      arxiv = {https://arxiv.org/abs/1806.02426}
    }
    
  2. 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. In 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},
      file = {../assets/pdf/rainforth2018tighter.pdf},
      arxiv = {https://arxiv.org/abs/1802.04537}
    }
    
  3. Le, T. A., Igl, M., Rainforth, T., Jin, T., & Wood, F. (2018). Auto-Encoding Sequential Monte Carlo. In 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},
      file = {../assets/pdf/le2018autoencoding.pdf},
      arxiv = {https://arxiv.org/abs/1802.04537}
    }
    
  4. Le, T. A., Baydin, A. G., Zinkov, R., & Wood, F. (2017). Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. In 30th International Joint Conference on Neural Networks (pp. 3514–3521). Anchorage, AK, USA: IEEE.
    @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},
      file = {../assets/pdf/le2017using.pdf},
      arxiv = {https://arxiv.org/abs/1703.00868}
    }
    
  5. Le, T. A., Baydin, A. G., & Wood, F. (2017). Inference Compilation and Universal Probabilistic Programming. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Vol. 54, pp. 1338–1348). Fort Lauderdale, FL, USA: PMLR.
    @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},
      file = {../assets/pdf/le2017inference.pdf},
      link = {http://proceedings.mlr.press/v54/},
      arxiv = {https://arxiv.org/abs/1610.09900}
    }
    
  6. Rainforth, T., Le, T. A., van de Meent, J.-W., Osborne, M. A., & Wood, F. (2016). Bayesian Optimization for Probabilistic Programs. In Advances in Neural Information Processing Systems (pp. 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},
      file = {../assets/pdf/rainforth2016bayesian_main.pdf},
      supplements = {../assets/pdf/rainforth2016bayesian_supp.pdf},
      video = {https://www.youtube.com/watch?v=gVzV-NxKa9U},
      link = {https://papers.nips.cc/paper/6421-bayesian-optimization-for-probabilistic-programs},
      code = {https://github.com/probprog/bopp}
    }
    

Workshop publications

  1. Casado, M. L., Baydin, A. G., Rubio David Martı́nez, Le, T. A., Wood, F., Heinrich, L., … Prabhat. (2017). Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. In NIPS 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 = {NIPS Workshop on Deep Learning for Physical Sciences},
      year = {2017},
      arxiv = {https://arxiv.org/abs/1712.07901}
    }
    
  2. 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]. In NIPS 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 = {NIPS Workshop on Bayesian Deep Learning},
      title = {Tighter Variational Bounds are Not Necessarily Better [Workshop Version]},
      year = {2017},
      file = {../assets/pdf/rainforth2017tighter.pdf}
    }
    
  3. Le, T. A., Baydin, A. G., & Wood, F. (2016). Nested Compiled Inference for Hierarchical Reinforcement Learning. In NIPS 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 = {NIPS Workshop on Bayesian Deep Learning},
      title = {Nested Compiled Inference for Hierarchical Reinforcement Learning},
      year = {2016},
      file = {../assets/pdf/le2016nested.pdf}
    }
    
  4. Perov, Y., Le, T. A., & Wood, F. (2015). Data-driven Sequential Monte Carlo in Probabilistic Programming. In NIPS Workshop on Black Box Learning and Inference.
    @inproceedings{perov2015datadriven,
      author = {Perov, Yura and Le, Tuan Anh and Wood, Frank},
      booktitle = {NIPS 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}
    }