Max Planck Research Group Leader

Office: 223

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

+49 7071 601 572

+49 7071 601 552

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

41 results
(BibTeX)

**Batch Bayesian Optimization via Local Penalization**
*Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016)*, 51, pages: 648-657, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C.), 2016 (conference)

**Active Uncertainty Calibration in Bayesian ODE Solvers**
*Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)*, pages: 309-318, (Editors: Ihler, A. and Janzing, D.), AUAI Press, 2016 (conference)

**Probabilistic Approximate Least-Squares**
*Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016)*, 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), 2016 (conference)

**Automatic LQR Tuning Based on Gaussian Process Global Optimization**
In *Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)*, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

**Dual Control for Approximate Bayesian Reinforcement Learning**
*Journal of Machine Learning Research*, 17(127):1-30, 2016 (article)

**Gaussian Process Based Predictive Control for Periodic Error Correction **
*IEEE Transactions on Control Systems Technology *, 24(1):110-121, 2016 (article)

**Probabilistic Line Searches for Stochastic Optimization**
In *Advances in Neural Information Processing Systems 28*, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

**Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results**
*Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems*, September 2015 (conference)

**Probabilistic numerics and uncertainty in computations**
*Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences*, 471(2179), 2015 (article)

**A Random Riemannian Metric for Probabilistic Shortest-Path Tractography**
In *18th International Conference on Medical Image Computing and Computer Assisted Intervention*, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

**Inference of Cause and Effect with Unsupervised Inverse Regression**
In *Proceedings of the 18th International Conference on Artificial Intelligence and Statistics*, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

**Probabilistic Interpretation of Linear Solvers**
*SIAM Journal on Optimization*, 25(1):234-260, 2015 (article)

**Probabilistic Progress Bars**
In *Conference on Pattern Recognition (GCPR)*, 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

**Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics**
In *Proceedings of the 17th International Conference on Artificial Intelligence and Statistics*, 33, pages: 347-355, JMLR: Workshop and Conference Proceedings, (Editors: S Kaski and J Corander), Microtome Publishing, Brookline, MA, AISTATS, April 2014 (inproceedings)

**Probabilistic ODE Solvers with Runge-Kutta Means**
In *Advances in Neural Information Processing Systems 27*, pages: 739-747, (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)

**Active Learning of Linear Embeddings for Gaussian Processes**
In *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence*, pages: 230-239, (Editors: NL Zhang and J Tian), AUAI Press , Corvallis, Oregon, UAI2014, 2014, another link: http://arxiv.org/abs/1310.6740 (inproceedings)

**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)