70 results
(View BibTeX file of all listed publications)

**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)

**Kernel Recursive ABC: Point Estimation with Intractable Likelihood**
*Proceedings of the 35th International Conference on Machine Learning*, pages: 2405-2414, PMLR, July 2018 (conference)

**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

**Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference**
*Arxiv e-prints*, arXiv:1805.08845v1 [stat.ML], 2018 (article)

**Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models**
*Arxiv e-prints*, arXiv:1409.5178v2 [stat.ML], 2018 (article)

**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)

**Probabilistic Approaches to Stochastic Optimization**
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

**Large sample analysis of the median heuristic**
2018 (misc) In preparation

**Probabilistic Ordinary Differential Equation Solvers — Theory and Applications**
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

**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)

**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)

**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)

**Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings**
*Arxiv e-prints*, arXiv:1709.00147v1 [math.NA], 2017 (article)

**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)

**Nonparametric Disturbance Correction and Nonlinear Dual Control**
(24098), ETH Zurich, 2017 (phdthesis)

**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)

Meier, F., Hennig, P., Schaal, S.
**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)

**Camera-specific Image Denoising**
Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

**Quasi-Newton Methods: A New Direction**
*Journal of Machine Learning Research*, 14(1):843-865, March 2013 (article)