Publications

• Gradient-Free Variational Learning With Conditional Mixture Networks

  NeurIPS Workshop on Bayesian Decision-making and Uncertainty, 2024
  Conor Heins, Hao Wu, Dimitrije Markovic, Alexander Tschantz, Jeff Beck, Christopher Buckley
  Paper Bibtex
  @article{heins2024gradient, title={Gradient-free variational learning with conditional mixture networks}, author={Heins, Conor and Wu, Hao and Markovic, Dimitrije and Tschantz, Alexander and Beck, Jeff and Buckley, Christopher}, journal={arXiv preprint arXiv:2408.16429}, year={2024} }
• Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm

  Conference on Neural Information Processing Systems (NeurIPS), 2024
  Eli Sennesh, Hao Wu, Tommaso Salvatori
  Paper Bibtex
  @article{sennesh2024divide,
  title={Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm},
  author={Sennesh, Eli and Wu, Hao and Salvatori, Tommaso},
  journal={arXiv preprint arXiv:2408.05834},
  year={2024}
  }
• Nested Variational Inference

  Conference on Neural Information Processing Systems (NeurIPS), 2021
  Symposium on Advances in Approximate Bayesian Inference (AABI), 2021
  Heiko Zimmermann, Hao Wu, Babak Esmaeili, Sam Stites, Jan-Willem van de Meent
  Paper Bibtex
  @article{zimmermann2021nested,
  title={Nested Variational Inference},
  author={Zimmermann, Heiko and Wu, Hao and Esmaeili, Babak and van de Meent, Jan-Willem},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
  }
• Learning Proposals for Probabilistic Programs with Inference Combinators

  Uncertainty in Artificial Intelligence (UAI), 2021
  Sam Stites*, Heiko Zimmermann*, Hao Wu, Eli Sennesh, Jan-Willem van de Meent
  Paper Bibtex
  @inproceedings{stites2021learning,
  title={Learning proposals for probabilistic programs with inference combinators},
  author={Stites, Sam and Zimmermann, Heiko and Wu, Hao and Sennesh, Eli and van de Meent, Jan-Willem},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={1056--1066},
  year={2021},
  organization={PMLR}
  }
• Conjugate Energy-Based Models

  International Conference on Machine Learning (ICML), 2021
  Symposium on Advances in Approximate Bayesian Inference (AABI), 2021
  Hao Wu*, Babak Esmaeili*, Michael Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent
  Paper Bibtex Talk
  @inproceedings{wu2021conjugate,
  title={Conjugate Energy-Based Models},
  author={Wu, Hao and Esmaeili, Babak and Wick, Michael L and Tristan, Jean-Baptiste and van de Meent, Jan-Willem},
  booktitle={International Conference on Machine Learning},
  volume = {139},
  pages = {11228--11239},
  year = {2021},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR}
  }
• Amortized Population Gibbs Samplers with Neural Sufficient Statistics

  International Conference on Machine Learning (ICML), 2020
  Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent
  Paper Bibtex Code Talk
  @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},
  pages={10421--10431},
  year={2020},
  organization={PMLR}
  }
• Structured Disentangled Representations

  Artificial Intelligence and Statistics (AISTATS), 2019
  Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem van de Meent
  Paper Bibtex
  @inproceedings{esmaeili2019structured,
  title={Structured disentangled representations},
  author={Esmaeili, Babak and Wu, Hao and Jain, Sarthak and Bozkurt, Alican and Siddharth, Narayanaswamy and Paige, Brooks and Brooks, Dana H and Dy, Jennifer and Meent, Jan-Willem},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={2525--2534},
  year={2019},
  organization={PMLR}
  }
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• Composing Modeling and Inference Operations with Probabilistic Program Combinators

  NeurIPS Workshop on Bayesian Nonparametrics, 2018
  Eli Sennesh, Adam Ścibior, Hao Wu, Jan-Willem van de Meent
  Paper Bibtex
  @article{sennesh2018composing,
  title={Composing Modeling and Inference Operations with Probabilistic Program Combinators},
  author={Sennesh, Eli and Wu, Hao and van de Meent, Jan-Willem},
  booktitle={Workshop on Bayesian Nonparametrics at Advances in Neural Information Processing Systems},
  year={2018} }