Office: S2.006

Max-Planck-Ring 4

72076 Tübingen

Germany

Max-Planck-Ring 4

72076 Tübingen

Germany

+49 7071 601 572

+49 7071 601 552

On 1 May 2018, I was appointed as a Full Professor to the newly created Chair for the Methods of Machine Learning at the Computer Science department of the University of Tübingen. **Please find my new page there.** I am also keeping an adjunct position at the Max Planck Institute for Intelligent Systems.

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.
- A while ago I was 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).

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 holds the Chair for the Methods of Machine Learning at the University of Tübingen, Germany, and an adjunct position at the Max Planck Institute for Intelligent Systems. He studied physics in Heidelberg and at Imperial College, London, and received a PhD from the University of Cambridge, UK, in 2011, under the supervision of the late Sir David JC MacKay. Since that time, he has been interested in the notion of computation as information gathering and, with collaborators, has helped re-establish the field of probabilistic numerics. Philipp primarily works in the machine learning community, where his group has made several algorithmic contributions. He has held an Emmy Noether fellowship, a Max Planck Research grant, and, in 2017, was awarded an ERC Starting Grant by the European Commission.*

Inference Probability Numerical Methods

68 results
(View BibTeX file of all listed publications)

**Limitations of the empirical Fisher approximation for natural gradient descent**
*Advances in Neural Information Processing Systems 32*, pages: 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

**Convergence Guarantees for Adaptive Bayesian Quadrature Methods**
*Advances in Neural Information Processing Systems 32*, pages: 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

**DeepOBS: A Deep Learning Optimizer Benchmark Suite**
*7th International Conference on Learning Representations (ICLR)*, May 2019 (conference)

**Fast and Robust Shortest Paths on Manifolds Learned from Data**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

**Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

**Probabilistic Linear Solvers: A Unifying View**
*Statistics and Computing*, 2019 (article) Accepted

**Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective**
*ArXiv preprint 2018*, arXiv:1810.03440 [stat.ME], October 2018 (article)

**Convergence Rates of Gaussian ODE Filters**
*arXiv preprint 2018*, arXiv:1807.09737 [math.NA], July 2018 (article)

**Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences**
*Arxiv e-prints*, arXiv:1805.08845v1 [stat.ML], 2018 (article)

**Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients**
In *Proceedings of the 35th International Conference on Machine Learning (ICML)*, 2018 (inproceedings) Accepted

**A probabilistic model for the numerical solution of initial value problems**
*Statistics and Computing*, Springer US, 2018 (article)

**Analytical incorporation of fractionation effects in probabilistic treatment planning for intensity-modulated proton therapy**
*Medical Physics*, 2018 (article)

**On the Design of LQR Kernels for Efficient Controller Learning**
*Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC)*, pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

**Probabilistic Line Searches for Stochastic Optimization**
*Journal of Machine Learning Research*, 18(119):1-59, November 2017 (article)

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

**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, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

**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)

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

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

Hennig, P.
**Computing with Uncertainty**
2017 (mpi_year_book)

**Fast Bayesian hyperparameter optimization on large datasets**
*Electronic Journal of Statistics*, 11, 2017 (article)

**New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)**
*Dagstuhl Reports*, 6(11):142-167, 2017 (book)

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

**Analytical probabilistic modeling of RBE-weighted dose for ion therapy**
*Physics in Medicine and Biology (PMB)*, 62(23):8959-8982, 2017 (article)

**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)

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

**Automatic LQR Tuning Based on Gaussian Process Global Optimization**
In *Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)*, pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

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

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

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

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

**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 (iROS)*, pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

**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 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)

**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)

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

**Local Gaussian Regression**
*arXiv preprint*, March 2014, clmc (misc)

**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)

**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)

**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)

**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)