ei Mastakouri, A., Schölkopf, B., Janzing, D. 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

[BibTex]

ei von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B. 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

link (url) [BibTex]

ei von Kügelgen, J., Rubenstein, P., Schölkopf, B., Weller, A. 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

link (url) [BibTex]

ei Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M. 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

[BibTex]

ei Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B. Convolutional neural networks: A magic bullet for gravitational-wave detection? Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)

link (url) DOI [BibTex]

ei Babbar, R., Schölkopf, B. Data scarcity, robustness and extreme multi-label classification Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

DOI [BibTex]

ei Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M. Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance Engineering in Medicine and Biology Conference (EMBC), July 2019 (conference) Accepted

arXiv PDF [BibTex]

ei Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B. 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)

link (url) [BibTex]

ei Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R. 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)

link (url) [BibTex]

ei Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B. 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)

link (url) [BibTex]

ei Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z. 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)

link (url) [BibTex]

ei Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B. 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)

PDF link (url) [BibTex]

ei Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O. 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)

PDF link (url) [BibTex]

ei ps Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H. 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)

Code video demo pdf link (url) [BibTex]

ei Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B. Generate Semantically Similar Images with Kernel Mean Matching 6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference) Accepted

[BibTex]

ei Akrour, R., Pajarinen, J., Peters, J., Neumann, G. 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)

link (url) [BibTex]

ei Becker-Ehmck, P., Peters, J., van der Smagt, P. 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)

link (url) [BibTex]

ei Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S. 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)

PDF link (url) [BibTex]

ei Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D. 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)

PDF link (url) [BibTex]

ei Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E. 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)

PDF link (url) [BibTex]

ei Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R. Meta learning variational inference for prediction 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

arXiv link (url) [BibTex]

ei Lutter, M., Ritter, C., Peters, J. Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

ei pn Schneider, F., Balles, L., Hennig, P. DeepOBS: A Deep Learning Optimizer Benchmark Suite 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

ei Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S. 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)

link (url) [BibTex]

ei Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G. SOM-VAE: Interpretable Discrete Representation Learning on Time Series 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

ei Bauer, M., Mnih, A. Resampled Priors for Variational Autoencoders 22nd International Conference on Artificial Intelligence and Statistics, April 2019 (conference) Accepted

arXiv [BibTex]

ei von Kügelgen, J., Mey, A., Loog, M. 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)

PDF link (url) [BibTex]

ei Mroueh, Y., Sercu, T., Raj, A. 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)

PDF link (url) [BibTex]

ei pn Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M. 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)

PDF link (url) [BibTex]

pn ei de Roos, F., Hennig, P. 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)

PDF link (url) [BibTex]

ei Wenk, P., Gotovos, A., Bauer, S., Gorbach, N., Krause, A., Buhmann, J. M. 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)

PDF PDF link (url) [BibTex]

ei Rojas-Carulla, M. Learning Transferable Representations University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

ei Gu, S. Sample-efficient deep reinforcement learning for continuous control University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

ei Raj, A., Gresele, L., Besserve, M., Schölkopf, B., Bauer, S. Orthogonal Structure Search for Efficient Causal Discovery from Observational Data 2019 (misc)

arXiv [BibTex]

ei Aghaeifar, A., Zhou, J., Heule, R., Tabibian, B., Schölkopf, B., Jia, F., Zaitsev, M., Scheffler, K. A 32-channel multi-coil setup optimized for human brain shimming at 9.4T Magnetic Resonance in Medicine, 2019, (Early View) (article)

DOI [BibTex]

ei Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H. More Powerful Selective Kernel Tests for Feature Selection 2019 (misc) Submitted

arXiv [BibTex]

ei Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R. P. Multidimensional Contrast Limited Adaptive Histogram Equalization IEEE Access, 7, pages: 165437-165447, 2019 (article)

arXiv link (url) DOI [BibTex]

ei Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M. Enhancing Human Learning via Spaced Repetition Optimization Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

DOI Project Page Project Page [BibTex]

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

[BibTex]

ei Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K. Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces 2019 (conference) Submitted

arXiv [BibTex]

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

[BibTex]

ei Büchler, D., Calandra, R., Peters, J. Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots 2019 (article) Submitted

Arxiv Video [BibTex]

ei Abbati*, G., Wenk*, P., Osborne, M. A., Krause, A., Schölkopf, B., Bauer, S. 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)

PDF link (url) [BibTex]

ei Weichwald, S. Pragmatism and Variable Transformations in Causal Modelling ETH Zurich, 2019 (phdthesis)

[BibTex]

ei Meding, K., Schölkopf, B., Wichmann, F. A. Perception of temporal dependencies in autoregressive motion European Conference on Visual Perception (ECVP), 2019 (poster)

[BibTex]

ei Park, M., Jitkrittum, W. ABCDP: Approximate Bayesian Computation Meets Differential Privacy 2019 (misc) Submitted

arXiv [BibTex]

ei Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M. S. B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J. Inferring causation from time series with perspectives in Earth system sciences Nature Communications, 2019 (article) In revision

[BibTex]

ei Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W. 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

[BibTex]

ei Katiyar, P. Quantification of tumor heterogeneity using PET/MRI and machine learning Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

[BibTex]

ei Hohmann, M. R., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B. MYND: A Platform for Large-scale Neuroscientific Studies Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

[BibTex]