Max Planck Research Group Leader

Office: S2.006

Spemannstr. 34

72076 Tübingen

Germany

Spemannstr. 34

72076 Tübingen

Germany

+49 7071 601 572

I am interested in algorithms that estimate and express uncertainty about the result of imprecise computations. Such imprecision can arise because the computational task is not analytically tractable, because a limited computational budget only allows a partial solution, or because the description of the task is itself imprecise to begin with. Probability measures provide the formal language for the description of such uncertainty. My group and I develop computer algorithms that take in and return probability measures; we call these probabilistic numerical methods.

- For detailed information about our work, please see our group's homepage.
- A general overview of probabilistic numerical methods can be found at http://www.probabilistic-numerics.org.
- I was recently interviewed by Katie Gorman and Ryan Adams for Talking Machines. The interview provides a bit of a general introduction. In a bittersweet coincidence, this episode of the podcast opens with an obituary for my incredible PhD advisor, Sir David MacKay.
- It usually relatively up to date. The list of publications on this webpage, however, is usually more reliable.

- Video of my talk on Bayesian Optimization at the Gaussian Process Summer School 2015
- I organised the Machine Learning Summer School 2015 (videos and slides). During this MLSS, I also did a tutorial on probabilistic numerics
- Videos of my Gaussian Process tutorial at the RNLS summer school at the ETH Zürich are online. This is the most recent and most compact video of my GP tutorial. If you want to learn about GPs in 90 minutes, I recommend watching this one instead of the older ones listed below.
- I organised the Machine Learning Summer School 2013 (videos and slides)
- Videos of my talks at the MLSS are up (slide animations only work in Adobe Reader):
- I spoke on GPs at the Gaussian Process Winter School 2013. Here is a video, and the slides of this talk (slide animations only work in Adobe Reader).

can be found here. Don't trust it to always be up to date. If you need a bio-blurb for your event web-page or a talk introduction, here's a suggestion (sorry if this sounds like grandstanding, I've repeatedly been asked for such a text):

*Philipp Hennig heads the Max Planck Research Group on Probabilistic Numerics at the MPI for Intelligent Systems in Tübingen, Germany. He studied physics in Heidelberg and London, before he moved to Cambridge, UK, where he did his PhD in David MacKay's inference group. Since that time, he is interested in the information content of computations, and mathematical notions of uncertainty for deterministic computation. Together with two colleagues from Oxford and Columbia U, he organized the inaugural workshop on Probabilistic Numerics in 2012, (re-) starting a community effort to provide a rigorous formulation of computation as the collection of information by autonomous, self-consistent agents. His work has been published in the leading venues of machine learning, as well as journals of the applied mathematics community. Together with his group, he has provided novel interpretations of classic numerical algorithms as maximum a-posteriori estimators, and used these results to create new algorithmical tools for machine learning and artificial intelligence.*

Inference Probability Numerical Methods

45 results
(BibTeX)

**Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers**
In *Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Lecture Notes in Computer Science Vol. 8675*, pages: 265-272, (Editors: P. Golland, N. Hata, C. Barillot, J. Hornegger and R. Howe), Springer, Heidelberg, MICCAI, 2014 (inproceedings)

Meier, F., Hennig, P., Schaal, S.
**Local Gaussian Regression**
*arXiv preprint*, March 2014, clmc (misc)

**Incremental Local Gaussian Regression**
In *Advances in Neural Information Processing Systems 27*, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

**Efficient Bayesian Local Model Learning for Control**
In *Proceedings of the IEEE International Conference on Intelligent Robots and Systems*, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

**Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature**
In *Advances in Neural Information Processing Systems 27*, pages: 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

**Quasi-Newton Methods: A New Direction**
*Journal of Machine Learning Research*, 14(1):843-865, March 2013 (article)

**The Randomized Dependence Coefficient**
In *Advances in Neural Information Processing Systems 26*, pages: 1-9, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

**Fast Probabilistic Optimization from Noisy Gradients**
In *Proceedings of The 30th International Conference on Machine Learning, JMLR W&CP 28(1)*, pages: 62–70, (Editors: S Dasgupta and D McAllester), ICML, 2013 (inproceedings)

**The Randomized Dependence Coefficient**
Neural Information Processing Systems (NIPS), 2013 (poster)

**Nonparametric dynamics estimation for time periodic systems**
In *Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing*, pages: 486-493 , 2013 (inproceedings)

**Analytical probabilistic modeling for radiation therapy treatment planning**
*Physics in Medicine and Biology*, 58(16):5401-5419, 2013 (article)

**Analytical probabilistic proton dose calculation and range uncertainties**
In *17th International Conference on the Use of Computers in Radiation Therapy*, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

**Animating Samples from Gaussian Distributions**
(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

**Quasi-Newton Methods: A New Direction**
In *Proceedings of the 29th International Conference on Machine Learning*, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

**Kernel Topic Models**
In *Fifteenth International Conference on Artificial Intelligence and Statistics*, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

**Approximate Gaussian Integration using Expectation Propagation**
In pages: 1-11, -, January 2012 (inproceedings) Submitted

**Learning Tracking Control with Forward Models**
In pages: 259 -264, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

**Entropy Search for Information-Efficient Global Optimization**
*Journal of Machine Learning Research*, 13, pages: 1809-1837, -, June 2012 (article)

**Optimal Reinforcement Learning for Gaussian Systems**
In *Advances in Neural Information Processing Systems 24*, pages: 325-333, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

**Coherent Inference on Optimal Play in Game Trees**
In *JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010*, pages: 326-333, (Editors: Teh, Y.W. , M. Titterington ), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)