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.
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
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Schneider, F., Balles, L., Hennig, P.
DeepOBS: A Deep Learning Optimizer Benchmark Suite
7th International Conference on Learning Representations (ICLR), May 2019 (conference)
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Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.
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)
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de Roos, F., Hennig, P.
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)
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Bartels, S., Cockayne, J., Ipsen, I. C. F., Hennig, P.
Probabilistic Linear Solvers: A Unifying View
Statistics and Computing, 2019 (article) Accepted
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Tronarp, F., Kersting, H., Särkkä, S., Hennig, P.
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)
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Kersting, H., Sullivan, T. J., Hennig, P.
Convergence Rates of Gaussian ODE Filters
arXiv preprint 2018, arXiv:1807.09737 [math.NA], July 2018 (article)
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Kanagawa, M., Hennig, P., Sejdinovic, D., Sriperumbudur, B. K.
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)
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Balles, L., Hennig, P.
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 (inproceedings) Accepted
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Schober, M., Särkkä, S., Philipp Hennig,
A probabilistic model for the numerical solution of initial value problems
Statistics and Computing, Springer US, 2018 (article)
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Wahl, N., Hennig, P., Wieser, H., Bangert, M.
Analytical incorporation of fractionation effects in probabilistic treatment planning for intensity-modulated proton therapy
Medical Physics, 2018 (article)
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Marco, A., Hennig, P., Schaal, S., Trimpe, S.
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)
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Mahsereci, M., Hennig, P.
Probabilistic Line Searches for Stochastic Optimization
Journal of Machine Learning Research, 18(119):1-59, November 2017 (article)
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Balles, L., Romero, J., Hennig, P.
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)
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Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.
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)
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Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.
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)
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Mahsereci, M., Balles, L., Lassner, C., Hennig, P.
Early Stopping Without a Validation Set
arXiv preprint arXiv:1703.09580, 2017 (article)
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Roos, F. D., Hennig, P.
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)
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Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.
Fast Bayesian hyperparameter optimization on large datasets
Electronic Journal of Statistics, 11, 2017 (article)
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Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B.
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)
Dagstuhl Reports, 6(11):142-167, 2017 (book)
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Wahl, N., Hennig, P., Wieser, H. P., Bangert, M.
Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy
Physics in Medicine & Biology, 62(14):5790-5807, 2017 (article)
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Wieser, H., Hennig, P., Wahl, N., Bangert, M.
Analytical probabilistic modeling of RBE-weighted dose for ion therapy
Physics in Medicine and Biology (PMB), 62(23):8959-8982, 2017 (article)
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Klenske, E. D., Hennig, P., Schölkopf, B., Zeilinger, M. N.
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)
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Kersting, H., Hennig, P.
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)
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Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.
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)
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González, J., Dai, Z., Hennig, P., Lawrence, N.
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)
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Bartels, S., Hennig, P.
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)
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Klenske, E. D., Zeilinger, M., Schölkopf, B., Hennig, P.
Gaussian Process-Based Predictive Control for Periodic Error Correction
IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (article)
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Klenske, E. D., Hennig, P.
Dual Control for Approximate Bayesian Reinforcement Learning
Journal of Machine Learning Research, 17(127):1-30, 2016 (article)
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Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.
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)
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Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.
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)
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Hennig, P.
Probabilistic Interpretation of Linear Solvers
SIAM Journal on Optimization, 25(1):234-260, 2015 (article)
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Mahsereci, M., Hennig, P.
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)
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Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.
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)
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Hennig, P., Osborne, M. A., Girolami, M.
Probabilistic numerics and uncertainty in computations
Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)
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Kiefel, M., Schuler, C., Hennig, P.
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)
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Hennig, P., Hauberg, S.
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)
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Meier, F., Hennig, P., Schaal, S.
Local Gaussian Regression
arXiv preprint, March 2014, clmc (misc)
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Schober, M., Duvenaud, D., Hennig, P.
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)
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Garnett, R., Osborne, M., Hennig, P.
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)
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Schober, M., Kasenburg, N., Feragen, A., Hennig, P., Hauberg, S.
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)
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Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S.
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)
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Meier, F., Hennig, P., Schaal, S.
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)
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Meier, F., Hennig, P., Schaal, S.
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)
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Hennig, P., Kiefel, M.
Quasi-Newton Methods: A New Direction
Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)
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Lopez-Paz, D., Hennig, P., Schölkopf, B.
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)
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Hennig, P.
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)
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Klenske, E., Zeilinger, M., Schölkopf, B., Hennig, P.
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)
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Lopez-Paz, D., Hennig, P., Schölkopf, B.
The Randomized Dependence Coefficient
Neural Information Processing Systems (NIPS), 2013 (poster)