My scientific interests are in the field of machine learning and inference from empirical data. In particular, I study kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years I have also become interested in methods for finding causal structures that underly statistical dependences. I have worked on a number of different applications of machine learning - in our field, you get "to play in everyone's backyard." Most recently, I have been trying to play in the backyard of astronomers and photographers.
I am heading the Department of Empirical Inference; take a look at our last formal Research Overview and Alumni List.
Many of my papers can downloaded if you click on the tab "publications;" alternatively, from arxiv or from http://www.kernel-machines.org/. Some additional information:
Machine Learning Causal Inference Artificial Intelligence Computational Photography Statistics
If you'd like to contact me, please consider these two notes:
1. I recently became co-editor-in-chief of JMLR. I work for JMLR because I believe in its open access model, but it takes a lot of time. During my JMLR term, please don't convince me to do other journal or grant reviewing duties.
2. I am not very organized with my e-mail so if you want to apply for a position in my lab, please send your application only to Sekretariat-Schoelkopf@tuebingen.mpg.de. Note that we do not respond to non-personalized applications that look like they are being sent to a large number of places simultaneously.
We are always happy to receive outstanding applications for PhD positions and postdocs.
Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K.
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces
2019 (conference) Submitted
Mehrjou, A., Schölkopf, B.
Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)
Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)
Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.
Efficient Encoding of Dynamical Systems through Local Approximations
In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)
Jitkrittum, W., Kanagawa, H., Sangkloy, P., Hays, J., Schölkopf, B., Gretton, A.
Informative Features for Model Comparison
Advances in Neural Information Processing Systems 31, pages: 816-827, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)
Büchler, D., Calandra, R., Schölkopf, B., Peters, J.
Control of Musculoskeletal Systems using Learned Dynamics Models
IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)
Kim, T. H., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.
Spatio-temporal Transformer Network for Video Restoration
15th European Conference on Computer Vision (ECCV), Part III, 11207, pages: 111-127, Lecture Notes in Computer Science, (Editors: Vittorio Ferrari, Martial Hebert,Cristian Sminchisescu and Yair Weiss), Springer, September 2018 (conference)
Rubenstein, P. K., Bongers, S., Schölkopf, B., Mooij, J. M.
From Deterministic ODEs to Dynamic Structural Causal Models
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), August 2018 (conference)
Gondal, M. W., Schölkopf, B., Hirsch, M.
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution
Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), August 2018 (conference)
Huang, B., Zhang, K., Lin, Y., Schölkopf, B., C., G.
Generalized Score Functions for Causal Discovery
Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages: 1551-1560, (Editors: Yike Guo and Faisal Farooq), ACM, August 2018 (conference)
Janzing, D., Schölkopf, B.
Detecting non-causal artifacts in multivariate linear regression models
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2250-2258, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.
Learning-based solution to phase error correction in T2*-weighted GRE scans
1st International conference on Medical Imaging with Deep Learning (MIDL), July 2018 (conference)
Besserve, M., Sun, R., Schölkopf, B.
Intrinsic disentanglement: an invariance view for deep generative models
Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)
Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.
Tempered Adversarial Networks
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4448-4456, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Schölkopf, B.
Learning Independent Causal Mechanisms
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4033-4041, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Mehrjou, A., Schölkopf, B.
Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems
Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)
Balog, M., Tolstikhin, I., Schölkopf, B.
Differentially Private Database Release via Kernel Mean Embeddings
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 423-431, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Locatello, F., Raj, A., Praneeth Karimireddy, S., Rätsch, G., Schölkopf, B., Stich, S. U., Jaggi, M.
On Matching Pursuit and Coordinate Descent
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 3204-3213, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Wüthrich, M., Schölkopf, B.
Iterative Model-Fitting and Local Controller Optimization - Towards a Better Understanding of Convergence Properties
Workshop on Prediction and Generative Modeling in Reinforcement Learning at ICML, July 2018 (conference)
Tolstikhin, I., Bousquet, O., Gelly, S., Schölkopf, B.
Wasserstein Auto-Encoders
6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Dehghani, M., Mehrjou, A., Gouws, S., Kamps, J., Schölkopf, B.
Fidelity-Weighted Learning
6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.
Tempered Adversarial Networks
Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.
Learning Disentangled Representations with Wasserstein Auto-Encoders
Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Bauer, M., Volchkov, V., Hirsch, M., Schölkopf, B.
Automatic Estimation of Modulation Transfer Functions
IEEE International Conference on Computational Photography (ICCP), May 2018 (conference)
Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D.
Group invariance principles for causal generative models
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 557-565, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)
Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.
Cause-Effect Inference by Comparing Regression Errors
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) , 84, pages: 900-909, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)
Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pages: 324-332, (Editors: Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek), ACM, Febuary 2018 (conference)
Schölkopf, B.
Die kybernetische Revolution
15-Mar-2018, Süddeutsche Zeitung, 2018 (misc)
Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.
Adaptation and Robust Learning of Probabilistic Movement Primitives
IEEE Transactions on Robotics, 2018 (article) In revision
Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C.
Learning Causality and Causality-Related Learning: Some Recent Progress
National Science Review, 5(1):26-29, 2018 (article)
Babbar, R., Schölkopf, B.
Adversarial Extreme Multi-label Classification
2018 (conference) Submitted
Pfister*, N., Weichwald*, S., Bülmann, P., Schölkopf, B.
groupICA: Independent component analysis for grouped data
2018, *equal contribution (article) Submitted
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, 2018 (article) Accepted
Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.
Autofocusing-based phase correction
Magnetic Resonance in Medicine, 2018, Epub ahead (article)
Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.
Case series: Slowing alpha rhythm in late-stage ALS patients
Clinical Neurophysiology, 129(2):406-408, 2018 (article)
Besserve, M., Sun, R., Schölkopf, B.
Counterfactuals uncover the modular structure of deep generative models
2018 (conference) Submitted
Simon-Gabriel, C. J., Ollivier, Y., Schölkopf, B., Bottou, L., Lopez-Paz, D.
Adversarial Vulnerability of Neural Networks Increases with Input Dimension
2018 (conference) Submitted
Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.
Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test
Frontiers in Endocrinology, 9, pages: 82, 2018 (article)
Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.
Invariant Models for Causal Transfer Learning
Journal of Machine Learning Research, 19(36):1-34, 2018 (article)
Simon-Gabriel, C. J., Schölkopf, B.
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
Journal of Machine Learning Research, 19(44):1-29, 2018 (article)
Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.
Discriminative Transfer Learning for General Image Restoration
IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)
Pérez-Pellitero, E., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.
Photorealistic Video Super Resolution
Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), 2018 (poster)
Gu, S., Lillicrap, T., Turner, R. E., Ghahramani, Z., Schölkopf, B., Levine, S.
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Advances in Neural Information Processing Systems 30, pages: 3849-3858, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)
Parascandolo, G., Rojas-Carulla, M., Kilbertus, N., Schölkopf, B.
Learning Independent Causal Mechanisms
Workshop: Learning Disentangled Representations: from Perception to Control at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)
Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., Schölkopf, B.
Avoiding Discrimination through Causal Reasoning
Advances in Neural Information Processing Systems 30, pages: 656-666, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)
Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C. J., Schölkopf, B.
AdaGAN: Boosting Generative Models
Advances in Neural Information Processing Systems 30, pages: 5430-5439, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)
Gebhard, T., Kilbertus, N., Parascandolo, G., Harry, I., Schölkopf, B.
ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets
Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)
Bauer*, M., Rojas-Carulla*, M., Świątkowski, J. B., Schölkopf, B., Turner, R. E.
Discriminative k-shot learning using probabilistic models
Second Workshop on Bayesian Deep Learning at the 31st Conference on Neural Information Processing Systems , December 2017, *equal contribution (conference)
Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.
Optimizing human learning
Workshop on Teaching Machines, Robots, and Humans at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)