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.

- 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 studied Physics in Heidelberg and London, and received his PhD from the University of Cambridge, UK, in 2011. He now runs an independent research group at to the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His group develops numerical algorithms both for and as intelligent, autonomous systems. It has been influential in the emergence of the research area of probabilistic numerical methods. Hennig works primarily in the machine learning community, but also has ties to applied mathematics, control engineering, and statistics.*

Inference Probability Numerical Methods

52 results
(BibTeX)

**On the Design of LQR Kernels for Efficient Controller Learning**
*Proceedings of the 56th IEEE Conference on Decision and Control*, December 2017 (conference) Accepted

**Probabilistic Line Searches for Stochastic Optimization**
*arXiv preprint arXiv:1703.10034*, 2017 (article)

**Probabilistic Active Learning of Functions in Structural Causal Models**
2017 (conference) Submitted

**Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning**
*arXiv preprint arXiv:1706.00241*, 2017 (article)

**Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy**
*Physics in Medicine & Biology*, 62(14):5790-5807, 2017 (article)

**Follow the Signs for Robust Stochastic Optimization**
*arXiv preprint arXiv:1705.07774*, 2017 (article)

**Early Stopping Without a Validation Set**
*arXiv preprint arXiv:1703.09580*, 2017 (article)

**Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets**
*Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017)*, 54, pages: 528-536, Proceedings of Machine Learning Research, (Editors: Sign, Aarti and Zhu, Jerry), PMLR, April 2017 (conference)

**Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization**
In *Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)*, pages: 1557-1563, IEEE International Conference on Robotics and Automation, May 2017 (inproceedings)

**Coupling Adaptive Batch Sizes with Learning Rates**
In *Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI)*, pages: 410-419, (Editors: Gal Elidan and Kristian Kersting), 2017 (inproceedings)

**Approximate dual control maintaining the value of information with an application to building control**
In *European Control Conference (ECC)*, pages: 800-806, June 2016 (inproceedings)

**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)

**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)

**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)

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

**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)

**Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy **
In pages: 746-751 , (Editors: Draghici, S. , T.M. Khoshgoftaar, V. Palade, W. Pedrycz, M.A. Wani, X. Zhu), IEEE, Piscataway, NJ, USA, Ninth International Conference on Machine Learning and Applications (ICMLA), December 2010 (inproceedings)

**Approximate Inference in Graphical Models**
University of Cambridge, November 2010 (phdthesis)

**Expectation Propagation on the Maximum of Correlated Normal Variables**
Cavendish Laboratory: University of Cambridge, July 2009 (techreport)