283 results
(View BibTeX file of all listed publications)

**Selecting causal brain features with a single conditional independence test per feature**
*Advances in Neural Information Processing Systems 32*, 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference) Accepted

**Semi-supervised learning, causality, and the conditional cluster assumption**
*NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making*, December 2019 (poster) Accepted

**Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks**
*NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making*, December 2019 (poster) Accepted

**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

**Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory**
*Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI)*, pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

**The Sensitivity of Counterfactual Fairness to Unmeasured Confounding**
*Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI)*, pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

**The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA**
*Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI)*, pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

**Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning**
*Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI)*, pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

**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

**Projections for Approximate Policy Iteration Algorithms**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

**Switching Linear Dynamics for Variational Bayes Filtering**
*Proceedings of the 36th International Conference on Machine Learning (ICML)*, 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

**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)

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

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

**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)

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

**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**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

**Sobolev Descent**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 2976-2985, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

**Fast and Robust Shortest Paths on Manifolds Learned from Data**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

**Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

**Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs**
*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)*, 89, pages: 1351-1360, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

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

**Perception of temporal dependencies in autoregressive motion**
European Conference on Visual Perception (ECVP), 2019 (poster)

**Kernel Stein Tests for Multiple Model Comparison**
*Advances in Neural Information Processing Systems 32*, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

**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

**Phenomenal Causality and Sensory Realism**
European Conference on Visual Perception (ECVP), 2019 (poster)

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

**Fisher Efficient Inference of Intractable Models**
*Advances in Neural Information Processing Systems 32*, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

**Falsification and future performance**
In *Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence*, 7070, pages: 65-78, Lecture Notes in Computer Science, Springer, Berlin, Germany, Solomonoff 85th Memorial Conference, January 2013 (inproceedings)

**Feedback Error Learning for Rhythmic Motor Primitives**
In *Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013)*, pages: 1317-1322, 2013 (inproceedings)

**Gaussian Process Vine Copulas for Multivariate Dependence**
In *Proceedings of the 30th International Conference on Machine Learning, W&CP 28(2)*, pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Poster:
http://people.tuebingen.mpg.de/dlopez/papers/icml2013_gpvine_poster.pdf (inproceedings)

**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)

**On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit**
In *IEEE Conference on Computer Vision and Pattern Recognition*, pages: 1083-1090, IEEE, CVPR, 2013 (inproceedings)

**Output Kernel Learning Methods**
In *International Workshop on Advances in Regularization,
Optimization, Kernel Methods and Support Vector Machines: theory and applications*, ROKS, 2013 (inproceedings)

**Alignment-based Transfer Learning for Robot Models**
In *Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013)*, pages: 1-7, 2013 (inproceedings)

**Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method**
In *13th International Conference on Data Mining*, pages: 1003-1008, (Editors: H. Xiong, G. Karypis, B. M. Thuraisingham, D. J. Cook and X. Wu), IEEE Computer Society, ICDM, 2013 (inproceedings)

**A probabilistic approach to robot trajectory generation**
In *Proceedings of the 13th IEEE International Conference on Humanoid Robots (HUMANOIDS)*, pages: 477-483, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 (inproceedings)

**Geometric optimisation on positive definite matrices for elliptically contoured distributions**
In *Advances in Neural Information Processing Systems 26*, pages: 2562-2570, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

**Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC**
43rd Annual Meeting of the Society for Neuroscience (Neuroscience), 2013 (poster)

**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)