pn
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)
pn
Kajihara, T., Kanagawa, M., Yamazaki, K., Fukumizu, K.
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Proceedings of the 35th International Conference on Machine Learning, pages: 2405-2414, PMLR, July 2018 (conference)
pn
Kersting, H., Sullivan, T. J., Hennig, P.
Convergence Rates of Gaussian ODE Filters
arXiv preprint 2018, arXiv:1807.09737 [math.NA], July 2018 (article)
pn
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)
ev
Ma, L., Stueckler, J., Wu, T., Cremers, D.
Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform
arxiv, 2018, arXiv:1808.01834 (techreport)
ev
Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J.
Numerical Quadrature for Probabilistic Policy Search
IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)
pn
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
ei
pn
Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.
Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference
Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)
pn
Nishiyama, Y., Kanagawa, M., Gretton, A., Fukumizu, K.
Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models
Arxiv e-prints, arXiv:1409.5178v2 [stat.ML], 2018 (article)
pn
Schober, M., Särkkä, S., Philipp Hennig,
A probabilistic model for the numerical solution of initial value problems
Statistics and Computing, Springer US, 2018 (article)
pn
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)
ei
pn
Mahsereci, M.
Probabilistic Approaches to Stochastic Optimization
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)
ei
pn
slt
Garreau, D., Jitkrittum, W., Kanagawa, M.
Large sample analysis of the median heuristic
2018 (misc) In preparation
ei
pn
Schober, M.
Probabilistic Ordinary Differential Equation Solvers — Theory and Applications
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)
am
ics
pn
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)
pn
Mahsereci, M., Hennig, P.
Probabilistic Line Searches for Stochastic Optimization
Journal of Machine Learning Research, 18(119):1-59, November 2017 (article)
ps
pn
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)
ei
pn
Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.
Dynamic Time-of-Flight
Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 170-179, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)
am
ics
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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)
pn
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)
pn
Kanagawa, M., Sriperumbudur, B. K., Fukumizu, K.
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
Arxiv e-prints, arXiv:1709.00147v1 [math.NA], 2017 (article)
ps
pn
Mahsereci, M., Balles, L., Lassner, C., Hennig, P.
Early Stopping Without a Validation Set
arXiv preprint arXiv:1703.09580, 2017 (article)
pn
Roos, F. D., Hennig, P.
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
arXiv preprint arXiv:1706.00241, 2017 (article)
ei
pn
sf
Klenske, E. D.
Nonparametric Disturbance Correction and Nonlinear Dual Control
(24098), ETH Zurich, 2017 (phdthesis)
ei
pn
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)
pn
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)
pn
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)
ei
pn
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)
ei
pn
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)
am
ics
pn
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)
ei
pn
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)
ei
pn
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)
ei
pn
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)
ei
pn
Klenske, E. D., Hennig, P.
Dual Control for Approximate Bayesian Reinforcement Learning
Journal of Machine Learning Research, 17(127):1-30, 2016 (article)
am
ei
ics
pn
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)
ei
pn
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)
ei
pn
Hennig, P.
Probabilistic Interpretation of Linear Solvers
SIAM Journal on Optimization, 25(1):234-260, 2015 (article)
ei
pn
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)
ei
pn
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)
ei
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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)
ei
ps
pn
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)
ei
ps
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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)
am
pn
Meier, F., Hennig, P., Schaal, S.
Local Gaussian Regression
arXiv preprint, March 2014, clmc (misc)
ei
pn
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)
ei
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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)
ei
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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)
ei
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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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ei
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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)