45 results
(View BibTeX file of all listed publications)

**Deep Reinforcement Learning for Event-Triggered Control**
In *Proceedings of the 57th IEEE International Conference on Decision and Control (CDC)*, pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

**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)

**Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference**
*Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML) at ICML*, July 2018 (conference)

**Nonlinear decoding of a complex movie from the mammalian retina**
*PLOS Computational Biology*, 14(5):1-27, Public Library of Science, May 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)

**L4: Practical loss-based stepsize adaptation for deep learning**
In *Advances in Neural Information Processing Systems 31 (NeurIPS 2018)*, pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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

**Systematic self-exploration of behaviors for robots in a dynamical systems framework**
In *Proc. Artificial Life XI*, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

**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)

**Learning equations for extrapolation and control**
In *Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018*, 80, pages: 4442-4450, http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (inproceedings)

**Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns**
*Proceedings International Conference on Humanoid Robots*, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)

**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)

**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)

**Self-Exploration of the Stumpy Robot with Predictive Information Maximization**
In *Proc. From Animals to Animats, SAB 2014*, 8575, pages: 32-42, LNCS, Springer, 2014 (inproceedings)

**Robot Learning by Guided Self-Organization**
In *Guided Self-Organization: Inception*, 9, pages: 223-260, Emergence, Complexity and Computation, Springer Berlin Heidelberg, 2014 (incollection)

**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)

**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)

**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)

**The Randomized Dependence Coefficient**
Neural Information Processing Systems (NIPS), 2013 (poster)

**Analytical probabilistic modeling for radiation therapy treatment planning**
*Physics in Medicine and Biology*, 58(16):5401-5419, 2013 (article)

**Analytical probabilistic proton dose calculation and range uncertainties**
In *17th International Conference on the Use of Computers in Radiation Therapy*, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

**Animating Samples from Gaussian Distributions**
(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

**Behavior as broken symmetry in embodied self-organizing robots**
In *Advances in Artificial Life, ECAL 2013*, pages: 601-608, MIT Press, 2013 (incollection)

**Information Driven Self-Organization of Complex Robotic Behaviors**
*PLoS ONE*, 8(5):e63400, Public Library of Science, 2013 (article)

**Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis**
*Frontiers in Psychology*, 4(801), 2013 (article)

**Robustness of guided self-organization against sensorimotor disruptions**
*Advances in Complex Systems*, 16(02n03):1350001, 2013 (article)

**A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior**
*Algorithms*, 2(1):398-409, 2009 (article)

**Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers**
*Arxiv* (article)