53 results
(View BibTeX file of all listed publications)

**Kernel Conditional Moment Test via Maximum Moment Restriction**
*Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI)*, August 2020 (conference) Accepted

**Bayesian Online Prediction of Change Points**
*Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI)*, August 2020 (conference) Accepted

**Algorithmic Recourse: from Counterfactual Explanations to Interventions**
*37th International Conference on Machine Learning (ICML)*, July 2020 (conference) Submitted

**Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control**
*21rst IFAC World Congress*, July 2020 (conference) Accepted

**A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming**
*21rst IFAC World Congress*, July 2020 (conference) Accepted

**Model-Agnostic Counterfactual Explanations for Consequential Decisions**
*Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)*, June 2020 (conference) Accepted

**A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization**
*Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)*, June 2020 (conference) Accepted

**Kernel Conditional Density Operators**
*Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)*, Proceedings of Machine Learning Research, June 2020 (conference) Accepted

**A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control**
*2nd Annual Conference on Learning for Dynamics and Control (L4DC)*, June 2020 (conference) Accepted

**Disentangling Factors of Variations Using Few Labels**
*8th International Conference on Learning Representations (ICLR)*, April 2020 (conference)

**Mixed-curvature Variational Autoencoders**
*8th International Conference on Learning Representations (ICLR)*, April 2020 (conference)

**Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals**
*ICLR 2020 Workshop "Tackling Climate Change with Machine Learning"*, April 2020 (conference)

**From Variational to Deterministic Autoencoders**
*8th International Conference on Learning Representations (ICLR) *, April 2020, *equal contribution (conference) Accepted

**On Mutual Information Maximization for Representation Learning**
*8th International Conference on Learning Representations (ICLR)*, April 2020 (conference) Accepted

**Towards causal generative scene models via competition of experts**
*ICLR 2020 Workshop "Causal Learning for Decision Making"*, April 2020, *equal contribution (conference)

**More Powerful Selective Kernel Tests for Feature Selection **
*Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2020 (conference) To be published

**Computationally Tractable Riemannian Manifolds for Graph Embeddings**
*37th International Conference on Machine Learning (ICML)*, 2020 (conference) Submitted

**A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models **
*IEEE International Conference on Robotics and Automation (ICRA)*, 2020 (conference) Accepted

**Practical Accelerated Optimization on Riemannian Manifolds**
*37th International Conference on Machine Learning (ICML)*, 2020 (conference) Submitted

**Fair Decisions Despite Imperfect Predictions**
*Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2020 (conference) Accepted

**Constant Curvature Graph Convolutional Networks**
*37th International Conference on Machine Learning (ICML)*, 2020, *equal contribution (conference) Submitted

**Divide-and-Conquer Monte Carlo Tree Search for goal directed planning**
2020, *equal contribution (conference) Submitted

**Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), LNCS Vol. 2777**
*Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003)*, *COLT/Kernel 2003*, pages: 746, Springer, Berlin, Germany, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, November 2003, Lecture Notes in Computer Science ; 2777 (proceedings)

**On the Complexity of Learning the Kernel Matrix**
In *Advances in Neural Information Processing Systems 15*, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Cluster Kernels for Semi-Supervised Learning**
In *Advances in Neural Information Processing Systems 15*, pages: 585-592, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Mismatch String Kernels for SVM Protein Classification**
In *Advances in Neural Information Processing Systems 15*, pages: 1417-1424, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Kernel Dependency Estimation**
In *Advances in Neural Information Processing Systems 15*, pages: 873-880, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model**
In *Advances in Neural Information Processing Systems 15*, pages: 1319-1326, (Editors: Becker, S., S. Thrun and K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Clustering with the Fisher score**
In *Advances in Neural Information Processing Systems 15*, pages: 729-736, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Marginalized Kernels between Labeled Graphs**
In *20th International Conference on Machine Learning*, pages: 321-328, (Editors: Faucett, T. and N. Mishra), 20th International Conference on Machine Learning, August 2003 (inproceedings)

**Sparse Gaussian Processes: inference, subspace identification and model selection**
In *Proceedings*, pages: 1-6, (Editors: Van der Hof, , Wahlberg), The Netherlands, 13th IFAC Symposium on System Identifiaction, August 2003, electronical version; Index ThA02-2 (inproceedings)

**Adaptive, Cautious, Predictive control with Gaussian Process Priors**
In *Proceedings of the 13th IFAC Symposium on System Identification*, pages: 1195-1200, (Editors: Van den Hof, P., B. Wahlberg and S. Weiland), Proceedings of the 13th IFAC Symposium on System Identification, August 2003 (inproceedings)

**On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics**
In *Humanoids Proceedings*, pages: 0-0, Humanoids Proceedings, July 2003, electronical version (inproceedings)

**A case based comparison of identification with neural network and Gaussian process models.**
In *Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003*, 1, pages: 137-142, (Editors: Ruano, E.A.), Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS, April 2003 (inproceedings)

**On-Line One-Class Support Vector Machines. An Application to Signal Segmentation**
In *IEEE ICASSP Vol. 2*, pages: 709-712, IEEE ICASSP, April 2003 (inproceedings)

**The Kernel Mutual Information**
In *IEEE ICASSP Vol. 4*, pages: 880-883, IEEE ICASSP, April 2003 (inproceedings)

**Hierarchical Spatio-Temporal Morphable Models for Representation of
complex movements for Imitation Learning**
In *11th International Conference on Advanced Robotics*, (2):453-458, (Editors: Nunes, U., A. de Almeida, A. Bejczy, K. Kosuge and J.A.T. Machado), 11th International Conference on Advanced Robotics, January 2003 (inproceedings)

**Feature Selection for Support Vector Machines by Means of Genetic Algorithms**
In *15th IEEE International Conference on Tools with AI*, pages: 142-148, 15th IEEE International Conference on Tools with AI, 2003 (inproceedings)

**Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting**
In *IEEE International Conference on Acoustics, Speech and Signal Processing*, 2, pages: 701-704, IEEE International Conference on Acoustics, Speech and Signal Processing, 2003 (inproceedings)

**Unsupervised Clustering of Images using their Joint Segmentation**
In *The 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV 2003)*, pages: 1-24, 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV), 2003 (inproceedings)

**Kernel Methods and Their Applications to Signal Processing**
In *Proceedings. (ICASSP ‘03)*, Special Session on Kernel Methods, pages: 860 , ICASSP, 2003 (inproceedings)

**Predictive control with Gaussian process models**
In *Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool*, pages: 352-356, (Editors: Zajc, B. and M. Tkal), Proceedings of IEEE Region 8 Eurocon: Computer as a Tool, 2003 (inproceedings)

**Extension of the nu-SVM range for classification**
In *Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190*, 190, pages: 179-196, NATO Science Series III: Computer and Systems Sciences, (Editors: J Suykens and G Horvath and S Basu and C Micchelli and J Vandewalle), IOS Press, Amsterdam, 2003 (inbook)

**Distance-based classification with Lipschitz functions**
In *Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory*, pages: 314-328, (Editors: Schölkopf, B. and M.K. Warmuth), Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003 (inproceedings)

**An Introduction to Support Vector Machines**
In *Recent Advances and Trends in Nonparametric Statistics
*, pages: 3-17, (Editors: MG Akritas and DN Politis), Elsevier, Amsterdam, The Netherlands, 2003 (inbook)

**Statistical Learning and Kernel Methods in Bioinformatics**
In *Artificial Intelligence and Heuristic Methods in Bioinformatics*, 183, pages: 1-21, 3, (Editors: P Frasconi und R Shamir), IOS Press, Amsterdam, The Netherlands, 2003 (inbook)

**A Short Introduction to Learning with Kernels**
In *Proceedings of the Machine Learning Summer School, Lecture Notes in Artificial Intelligence, Vol. 2600*, pages: 41-64, LNAI 2600, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

**Bayesian Kernel Methods**
In *Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Lecture Notes in Computer Science, Vol. 2600*, LNAI 2600, pages: 65-117, 0, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Germany, 2003 (inbook)

**Stability of ensembles of kernel machines**
In 190, pages: 111-124, NATO Science Series III: Computer and Systems Science, (Editors: Suykens, J., G. Horvath, S. Basu, C. Micchelli and J. Vandewalle), IOS press, Netherlands, 2003 (inbook)

**Unsupervised Segmentation and Classification of Mixtures of Markovian Sources**
In *The 33rd Symposium on the Interface of Computing Science and Statistics (Interface 2001 - Frontiers in Data Mining and Bioinformatics)*, pages: 1-15, 33rd Symposium on the Interface of Computing Science and Statistics (Interface - Frontiers in Data Mining and Bioinformatics), 2001 (inproceedings)