132 results
(View BibTeX file of all listed publications)

**Neural Signatures of Motor Skill in the Resting Brain**
*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019)*, October 2019 (conference) Accepted

**Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance**
*Engineering in Medicine and Biology Conference (EMBC)*, July 2019 (conference) Accepted

**Kernel Mean Matching for Content Addressability of GANs**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

**Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

**Local Temporal Bilinear Pooling for Fine-grained Action Parsing**
In *Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)*, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

**Generate Semantically Similar Images with Kernel Mean Matching**
*6th Workshop Women in Computer Vision (WiCV) (oral presentation)*, June 2019, *equal contribution (conference) Accepted

**Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

**First-Order Adversarial Vulnerability of Neural Networks and Input Dimension**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

**Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models**
In *Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

**Meta learning variational inference for prediction**
*7th International Conference on Learning Representations (ICLR)*, May 2019 (conference) Accepted

**Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning**
*7th International Conference on Learning Representations (ICLR)*, May 2019 (conference) Accepted

**DeepOBS: A Deep Learning Optimizer Benchmark Suite**
*7th International Conference on Learning Representations (ICLR)*, May 2019 (conference) Accepted

**Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments**
*Deep Generative Models for Highly Structured Data Workshop at ICLR*, May 2019, *equal contribution (conference) Accepted

**SOM-VAE: Interpretable Discrete Representation Learning on Time Series**
*7th International Conference on Learning Representations (ICLR)*, May 2019 (conference) Accepted

**Resampled Priors for Variational Autoencoders**
*22nd International Conference on Artificial Intelligence and Statistics*, April 2019 (conference) Accepted

**Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features**
*22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, April 2019 (conference) Accepted

**Sobolev Descent**
*22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, April 2019 (conference) Accepted

**Fast and Robust Shortest Paths on Manifolds Learned from Data**
* 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, April 2019 (conference) Accepted

**Data scarcity, robustness and extreme multi-label classification**
*Machine Learning*, Special Issue of the ECML PKDD 2019 Journal Track, March 2019 (article)

**Learning Transferable Representations**
University of Cambridge, UK, 2019 (phdthesis)

**Sample-efficient deep reinforcement learning for continuous control**
University of Cambridge, UK, 2019 (phdthesis)

**Enhancing Human Learning via Spaced Repetition Optimization**
*Proceedings of the National Academy of Sciences*, 2019, PNAS published ahead of print January 22, 2019 (article)

**Formally justified and modular Bayesian inference for probabilistic programs**
University of Cambridge, UK, 2019 (phdthesis)

**Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces**
2019 (conference) Submitted

**Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing**
Technical University of Munich, Germany, 2019 (mastersthesis)

**Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots**
2019 (article) Submitted

**AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, 2019, *equal contribution (conference)

**Pragmatism and Variable Transformations in Causal Modelling**
ETH Zurich, 2019 (phdthesis)

**Inferring causation from time series with perspectives in Earth system sciences**
*Nature Communications*, 2019 (article) In revision

**Kernel Stein Tests for Multiple Model Comparison**
2019 (conference) Submitted

**MYND: A Platform for Large-scale Neuroscientific Studies**
*Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI)*, 2019 (conference) Accepted

**A Kernel Stein Test for Comparing Latent Variable Models**
2019 (conference) Submitted

**Fisher Efficient Inference of Intractable Models**
2019 (conference) Submitted

**Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs**
*22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2019 (conference) Accepted

**From Variational to Deterministic Autoencoders**
2019, *equal contribution (conference) Submitted

**Optimizing the Execution of Dynamic Robot Movements With Learning Control**
*IEEE Transactions on Robotics*, pages: 1-16, 2019 (article)

**Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework**
*IEEE Robotics and Automation Letters*, 4(2):1784-1791, 2019 (article)

**Kernel Methods for Measuring Independence**
*Journal of Machine Learning Research*, 6, pages: 2075-2129, December 2005 (article)

**Kernel ICA for Large Scale Problems**
In pages: -, NIPS Workshop on Large Scale Kernel Machines, December 2005 (inproceedings)

**Some thoughts about Gaussian Processes**
NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

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Chapelle, O.
**A taxonomy of semi-supervised learning algorithms**
Yahoo!, December 2005 (talk)

**A Unifying View of Sparse Approximate Gaussian Process Regression**
*Journal of Machine Learning Research*, 6, pages: 1935-1959, December 2005 (article)

**Popper, Falsification and the VC-dimension**
(145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)

**Extension to Kernel Dependency Estimation with Applications to Robotics**
Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

**Kernel methods for dependence testing in LFP-MUA**
35(689.17), 35th Annual Meeting of the Society for Neuroscience (Neuroscience), November 2005 (poster)

**Training Support Vector Machines with Multiple Equality Constraints **
In *Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720*, pages: 182-193, (Editors: JG Carbonell and J Siekmann), Springer, Berlin, Germany, ECML, November 2005 (inproceedings)

**Geometrical aspects of statistical learning theory**
Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

**Measuring Statistical Dependence with Hilbert-Schmidt Norms**
In *Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 3734*, pages: 63-78, (Editors: S Jain and H-U Simon and E Tomita), Springer, Berlin, Germany, 16th International Conference ALT, October 2005 (inproceedings)

**Maximal Margin Classification for Metric Spaces**
*Journal of Computer and System Sciences*, 71(3):333-359, October 2005 (article)

**An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron**
In *Algorithmic Learning Theory: 16th International Conference*, pages: 78-92, Algorithmic Learning Theory, October 2005 (inproceedings)