13 results
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**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)

**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**
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)

**Learning Tracking Control with Forward Models**
In pages: 259 -264, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

**Approximate Gaussian Integration using Expectation Propagation**
In pages: 1-11, -, January 2012 (inproceedings) Submitted

**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)

**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)