157 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

**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

**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**
*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)

**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)

**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)

**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)

**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

**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

**Global Biclustering of Microarray Data**
In *ICDMW 2006*, pages: 125-129, (Editors: Tsumoto, S. , C. W. Clifton, N. Zhong, X. Wu, J. Liu, B. W. Wah, Y.-M. Cheung), IEEE Computer Society, Los Alamitos, CA, USA, Sixth IEEE International Conference on Data Mining, December 2006 (inproceedings)

**Conformal Multi-Instance Kernels**
In *NIPS 2006 Workshop on Learning to Compare Examples*, pages: 1-6, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

**A Kernel Method for the Two-Sample-Problem**
20th Annual Conference on Neural Information Processing Systems (NIPS), December 2006 (talk)

**Ab-initio gene finding using machine learning**
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

**Reinforcement Learning by Reward-Weighted Regression**
NIPS Workshop: Towards a New Reinforcement Learning? , December 2006 (talk)

**Graph boosting for molecular QSAR analysis**
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

**Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions**
NIPS Workshop on Causality and Feature Selection, December 2006 (talk)

**Information-theoretic Metric Learning**
In *NIPS 2006 Workshop on Learning to Compare Examples*, pages: 1-5, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

**Pattern Mining in Frequent Dynamic Subgraphs**
In pages: 818-822, (Editors: Clifton, C.W.), IEEE Computer Society, Los Alamitos, CA, USA, Sixth International Conference on Data Mining (ICDM), December 2006 (inproceedings)

**Learning Optimal EEG Features Across Time, Frequency and Space**
NIPS Workshop on Current Trends in Brain-Computer Interfacing, December 2006 (talk)

**Semi-Supervised Learning**
Advanced Methods in Sequence Analysis Lectures, November 2006 (talk)

**3DString: a feature string kernel for 3D object classification on voxelized data**
In pages: 198-207, (Editors: Yu, P.S. , V.J. Tsotras, E.A. Fox, B. Liu), ACM Press, New York, NY, USA, 15th ACM International Conference on Information and Knowledge Management (CIKM), November 2006 (inproceedings)

**Adapting Spatial Filter Methods for Nonstationary BCIs**
In *IBIS 2006*, pages: 65-70, 2006 Workshop on Information-Based Induction Sciences, November 2006 (inproceedings)

**A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images**
IEEE Medical Imaging Conference, November 2006 (talk)

**Semi-Supervised Support Vector Machines and Application to Spam Filtering**
ECML Discovery Challenge Workshop, September 2006 (talk)

**A Linear Programming Approach for Molecular QSAR analysis**
In *MLG 2006*, pages: 85-96, (Editors: Gärtner, T. , G. C. Garriga, T. Meinl), International Workshop on Mining and Learning with Graphs, September 2006, Best Paper Award (inproceedings)

**Incremental Aspect Models for Mining Document Streams**
In *PKDD 2006*, pages: 633-640, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

**PALMA: Perfect Alignments using Large Margin Algorithms**
In *GCB 2006*, pages: 104-113, (Editors: Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics, September 2006 (inproceedings)