OxCSML : People : Bradley Gram-Hansen

Probabilistic Programming, Monte Carlo Methods, Variational Inference, Computational Sustainability, Quantum Information

I am DPhil Student on the Autonomous Intelligent Machines and Systems (AIMS) program at the University of Oxford supervised by the wonderful Yee Whye Teh, Atılım Güneş Baydin, Phil Torr and Tom Rainforth. My research interests lie between the intersection of probabilistic programming, computational sustainability and Machine learning for societal uses. I am also interested in Quantum machine learning, as my background is in quantum information science.

Publications

2019

A. G. Baydin , L. Heinrich , W. Bhimji , B. Gram-Hansen , G. Louppe , L. Shao , K. Cranmer , F. Wood , Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, Advances in Neural Information Processing Systems, NeurlPS 2019, 2019.

@article{baydin2018efficient,
  title = {Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model},
  author = {Baydin, Atilim Gunes and Heinrich, Lukas and Bhimji, Wahid and Gram-Hansen, Bradley and Louppe, Gilles and Shao, Lei and Cranmer, Kyle and Wood, Frank},
  journal = {Advances in Neural Information Processing Systems, NeurlPS 2019},
  year = {2019}
}

B. J. Gram-Hansen , P. Helber , I. Varatharajan , F. Azam , A. Coca-Castro , V. Kopackova , P. Bilinski , Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data, in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, 361–368.

@inproceedings{gram2019mapping,
  title = {Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data},
  author = {Gram-Hansen, Bradley J and Helber, Patrick and Varatharajan, Indhu and Azam, Faiza and Coca-Castro, Alejandro and Kopackova, Veronika and Bilinski, Piotr},
  booktitle = {Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},
  pages = {361--368},
  year = {2019},
  organization = {ACM}
}

B. Gram-Hansen , C. S. Witt , T. Rainforth , P. H. Torr , Y. W. Teh , A. G. Baydin , Hijacking Malaria Simulators with Probabilistic Programming, in International Conference on Machine Learning (ICML) AI for Social Good workshop (AI4SG), 2019.

@inproceedings{gram2019hijacking,
  title = {Hijacking Malaria Simulators with Probabilistic Programming},
  author = {Gram-Hansen, Bradley and de Witt, Christian Schr{\"o}der and Rainforth, Tom and Torr, Philip HS and Teh, Yee Whye and Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}}},
  booktitle = {International Conference on Machine Learning (ICML) AI for Social Good workshop (AI4SG)},
  year = {2019}
}

A. G. Baydin , L. Shao , W. Bhimji , L. Heinrich , L. Meadows , J. Liu , A. Munk , S. Naderiparizi , B. Gram-Hansen , G. Louppe , . others , Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, in Proceedings of the International Conference for High Performance Computing, SC 2019, 2019.

@inproceedings{baydin2019etalumis,
  title = {Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale},
  author = {Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}} and Shao, Lei and Bhimji, Wahid and Heinrich, Lukas and Meadows, Lawrence and Liu, Jialin and Munk, Andreas and Naderiparizi, Saeid and Gram-Hansen, Bradley and Louppe, Gilles and others},
  booktitle = {Proceedings of the International Conference for High Performance Computing, SC 2019},
  year = {2019}
}

A. Blackwell , T. Kohn , M. Erwig , A. G. Baydin , L. Church , J. Geddes , A. Gordon , M. Gorinova , B. Gram-Hansen , N. Lawrence , . others , Usability of Probabilistic Programming Languages, in Psychology of Programming Interest Group 30th Annual Workshop, PPIG 2019, 2019.

@inproceedings{blackwell2019usability,
  title = {Usability of Probabilistic Programming Languages},
  author = {Blackwell, Alan and Kohn, Tobias and Erwig, Martin and Baydin, Atilim Gunes and Church, Luke and Geddes, James and Gordon, Andy and Gorinova, Maria and Gram-Hansen, Bradley and Lawrence, Neil and others},
  booktitle = {Psychology of Programming Interest Group 30th Annual Workshop, PPIG 2019},
  year = {2019}
}

Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , A Low-Level Probabilistic Programming Language for Non-Differentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
Project: bigbayes

We develop a new Low-level, First-order Prob- abilistic Programming Language (LF-PPL) suited for models containing a mix of contin- uous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme, is in its ability to automatically distinguish the discontinuous and continuous parameters in the density func- tion, while further providing runtime checks of when discontinuity boundaries have been crossed. This enables the introduction of new inference engines that are able to exploit gra- dient information, while remaining efficient for models which are not everywhere differen- tiable. We demonstrate this ability by intro- ducing a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this lan- guage has a density with a sufficiently low measure of discontinuities to maintain the validity of the inference engine.
```
@article{zhou2018lfppla,
  title = {{A Low-Level Probabilistic Programming Language
    for Non-Differentiable Models}},
  author = {Zhou, Yuan and Gram-Hansen, Bradley and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
  year = {2019},
  journal = {International Conference on Artificial Intelligence and Statistics (AISTATS)}
}
```

Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models, in The 22nd International Conference on Artificial Intelligence and Statistics, 2019, 148–157.

Project: bigbayes

@inproceedings{zhou2019lf,
  title = {LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models},
  author = {Zhou, Yuan and Gram-Hansen, Bradley and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
  booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics},
  pages = {148--157},
  year = {2019}
}

2018

P. Helber , B. Gram-Hansen , I. Varatharajan , F. Azam , A. Coca-Castro , V. Kopackova , P. Bilinski , Generating Material Maps to Map Informal Settlements, in NeurlPS workshop on Machine Learning for the Developing World (ML4DW), 2018.

@inproceedings{helber2018generating,
  title = {Generating Material Maps to Map Informal Settlements},
  author = {Helber, Patrick and Gram-Hansen, Bradley and Varatharajan, Indhu and Azam, Faiza and Coca-Castro, Alejandro and Kopackova, Veronika and Bilinski, Piotr},
  booktitle = {NeurlPS workshop on Machine Learning for the Developing World (ML4DW)},
  year = {2018}
}

B. Gram-Hansen , Y. Zhou , T. Kohn , T. Rainforth , H. Yang , F. Wood , Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities, in International Conference on Probabilistic Programming, 2018.

@inproceedings{gram2018hamiltonian,
  title = {Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities},
  author = {Gram-Hansen, Bradley and Zhou, Yuan and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
  booktitle = {International Conference on Probabilistic Programming},
  year = {2018}
}

2015

B. J. Gram-Hansen , Electron-Proton Entanglement in the Hydrogen Atom, Master's thesis, 2015.

@mastersthesis{gramelec,
  title = {Electron-Proton Entanglement in the Hydrogen Atom},
  author = {Gram-Hansen, Bradley J},
  year = {2015}
}

2014

B. J. Gram-Hansen , An Insight Into: Quantum Random Walks, Master's thesis, 2014.

@mastersthesis{graminsight,
  title = {An Insight Into: Quantum Random Walks},
  author = {Gram-Hansen, Bradley J},
  year = {2014}
}

Software

2019

Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , A Low-Level Probabilistic Programming Language for Non-Differentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS). 2019.
Project: bigbayes

We develop a new Low-level, First-order Prob- abilistic Programming Language (LF-PPL) suited for models containing a mix of contin- uous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme, is in its ability to automatically distinguish the discontinuous and continuous parameters in the density func- tion, while further providing runtime checks of when discontinuity boundaries have been crossed. This enables the introduction of new inference engines that are able to exploit gra- dient information, while remaining efficient for models which are not everywhere differen- tiable. We demonstrate this ability by intro- ducing a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this lan- guage has a density with a sufficiently low measure of discontinuities to maintain the validity of the inference engine.
```
@software{zhou2018lfpplb,
  title = {{A Low-Level Probabilistic Programming Language for Non-Differentiable Models}},
  author = {Zhou, Yuan and Gram-Hansen, Bradley and Kohn, Tobias and Rainforth, Tom and Yang, Hongseok and Wood, Frank},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = {2019},
  bdsk-url-1 = {https://github.com/bradleygramhansen/PyLFPPL}
}
```