Bernhard Schölkopf

Director

Empirical Inference

Office: N4.019
Max-Planck-Ring 4
72076 Tübingen
Germany

+49 7071 601 551

+49 7071 601 552

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 (PDF publication list) can downloaded if you click on the tab "publications;" alternatively, from arxiv or from http://www.kernel-machines.org/. Some additional information:

We have written a book about causality that was just published as an open access title at MIT Press (PDF, with Jonas Peters and Dominik Janzing).
Photographs: HDR composite of the 2019 solar eclipse viewed from La Silla, including the moon lit by the earth, wide angle view, view of the Alps from the southern black forest, a rainbow in La Palma, a lunar eclipse in 2007, the Andromeda galaxy, the Milky Way on the Roque de los Muchachos, the North America Nebula, the constellation Orion with Barnard's loop, and finally a picture of a beautiful northern light, which I took a few years ago from the plane, on the way home from a conference in Vancouver. I always try to get a window seat when flying home from the North American west coast - it is surprizingly common to see northern lights. Looking at the night sky is a fascinating and humbling experience.
Some chapters of our book Learning with Kernels.
Review paper on kernel methods in the Annals of Statistics.
Short high-level introduction on statistical learnig theory (in German) that appeared in the 2004 Jahrbuch of the Max Planck Society.
Obituary for Alexej Chervonenkis (NIPS 2014).
I am a member of the LIGO scientific collaboration to detect gravitational waves
With the growing interest in (how to make money with) big data, machine learning has significantly gained in popularity. We have published an article in the German newspaper FAZ in January 2015, discussing some of the implications. Disclaimer: the newspaper added some text that appears above our names - this was not written or approved by us.
In March 2018, I published an article about the cybernetic revolution in the German newspaper SZ. It starts with the thesis that the current revolution is about processing (generating, converting, industrializing) information in much the same way the first two industrial revolutions dealt with processing (generating, converting, industrializing) energy. I have occasionally put forward this thesis (but I'm sure I am not the only one who thinks of it this way), for instance during a NYU symposium on the future of AI in January 2016 (here are some notes written by Max Tegmark). The article also provides recommendations on what Europe should do to keep up with the development.
A children's book
I do not engage in military research, and I believe AI/ML should not be used for aggressive military purposes. Open letter against autonomous AI weapons / open letter against a military collaboration of KAIST, with positive outcome / IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
My department and/or members of the department (incl. myself) receive funding from a number of sources including Max Planck, the DFG, the Alexander-von-Humboldt foundation, Amazon, Google, Bosch, Facebook, the BMBF (German Ministry of Science), the EU, the ETH Zürich, the Land Baden-Wuerttemberg, the Koerber foundation, CIFAR, and the Stanford Center on Philanthropy and Civil Society.

Machine Learning Causal Inference Artificial Intelligence Computational Photography Statistics

click here

M.Sc. in mathematics and Lionel Cooper Memorial Prize, University of London (1992)
Diplom in physics (Tübingen, 1994)
doctorate in computer science from the Technical University Berlin (1997); thesis on Support Vector Learning (main advisor: V. Vapnik, AT&T Bell Labs) won the annual dissertation prize of the German Association for Computer Science (GI)
scientific member of the Max Planck Society, 2001
awards won by his lab
J. K. Aggarwal Prize of the International Association for Pattern Recognition, 2006
Max Planck Research Award, 2011
Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, 2012
Royal Society Milner Award, 2014
Member of the German National Academy of Science (Leopoldina) (since 2017)
Distinguished Amazon Scholar (since 2017)
Fellow of the ACM (Association for Computing Machinery) (since 2018)
Gottfried-Wilhelm-Leibniz-Preis of the German Science Foundation (2018)
Honorarprofessor at the Technical University Berlin (computer science) and at the Eberhard-Karls University Tübingen (physics)
list of publications as of January 2015
"ISI highly cited" (added in 2010)
the h Index for Computer Science
Google Scholar page
co-editor-in-chief of JMLR
member of the boards of the NIPS foundation and of the International Machine Learning Society
PC member (e.g., NIPS, COLT, ICML, UAI, DAGM, CVPR, Snowbird Learning Workshop) and co-chair of various conferences (COLT'03, DAGM'04, NIPS'05, NIPS'06 and the first two kernel workshops).
co-founder of the Machine Learning Summer Schools
two-page CV: PDF.

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.

701 results (View BibTeX file of all listed publications)

2019

Neural Signatures of Motor Skill in the Resting Brain

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

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), October 2019 (conference) Accepted

[BibTex]

2019

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]

Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M.

Engineering in Medicine and Biology Conference (EMBC), July 2019 (conference) Accepted

arXiv PDF [BibTex]

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]

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

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

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]

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]

Kernel Mean Matching for Content Addressability of GANs

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

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]

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]

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

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

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]

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]

Generate Semantically Similar Images with Kernel Mean Matching

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

6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference) Accepted

[BibTex]

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]

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

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

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]

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]

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

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

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]

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]

Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments

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

Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference) Accepted

link (url) [BibTex]

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) Accepted

link (url) [BibTex]

Data scarcity, robustness and extreme multi-label classification

Babbar, R., Schölkopf, B.

Machine Learning, Special Issue of the ECML PKDD 2019 Journal Track, March 2019 (article)

DOI [BibTex]

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

DOI [BibTex]

Orthogonal Structure Search for Efficient Causal Discovery from Observational Data

Raj, A., Gresele, L., Besserve, M., Schölkopf, B., Bauer, S.

2019 (misc)

arXiv [BibTex]

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

arXiv [BibTex]

Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

DOI Project Page Project Page [BibTex]

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]

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K.

2019 (conference) Submitted

arXiv [BibTex]

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

arXiv [BibTex]

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

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

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]

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]

Inferring causation from time series with perspectives in Earth system sciences

Nature Communications, 2019 (article) In revision

[BibTex]

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]

Kernel Stein Tests for Multiple Model Comparison

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

2019 (conference) Submitted

[BibTex]

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W. Kernel Stein Tests for Multiple Model Comparison 2019 (conference) Submitted

[BibTex]

MYND: A Platform for Large-scale Neuroscientific Studies

Hohmann, M. R., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

[BibTex]

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]

From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract

Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

arXiv [BibTex]

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B. From Variational to Deterministic Autoencoders 2019, *equal contribution (conference) Submitted

arXiv [BibTex]

2018

Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

2018

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)

PDF link (url) [BibTex]

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.

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)

arXiv link (url) [BibTex]

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)

arXiv link (url) [BibTex]

Efficient Encoding of Dynamical Systems through Local Approximations

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

arXiv PDF DOI Project Page [BibTex]

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)

arXiv PDF DOI Project Page [BibTex]

Informative Features for Model Comparison

Jitkrittum, W., Kanagawa, H., Sangkloy, P., Hays, J., Schölkopf, B., Gretton, A.

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)

link (url) Project Page [BibTex]

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)

link (url) Project Page [BibTex]

Control of Musculoskeletal Systems using Learned Dynamics Models

Büchler, D., Calandra, R., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

Abstract

Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

RAL18final link (url) DOI Project Page [BibTex]

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)

RAL18final link (url) DOI Project Page [BibTex]

Spatio-temporal Transformer Network for Video Restoration

Kim, T. H., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

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)

DOI [BibTex]

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)

DOI [BibTex]

The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

Gondal, M. W., Schölkopf, B., Hirsch, M.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), September 2018 (conference)

arXiv URL [BibTex]

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), September 2018 (conference)

arXiv URL [BibTex]

From Deterministic ODEs to Dynamic Structural Causal Models

Rubenstein, P. K., Bongers, S., Schölkopf, B., Mooij, J. M.

Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), August 2018 (conference)

Arxiv link (url) [BibTex]

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)

Arxiv link (url) [BibTex]

Generalized Score Functions for Causal Discovery

Huang, B., Zhang, K., Lin, Y., Schölkopf, B., Glymour, C.

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)

link (url) DOI [BibTex]

Huang, B., Zhang, K., Lin, Y., Schölkopf, B., Glymour, C. 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)

link (url) DOI [BibTex]

Detecting non-causal artifacts in multivariate linear regression models

Janzing, D., Schölkopf, B.

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)

link (url) [BibTex]

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)

link (url) [BibTex]

Learning-based solution to phase error correction in T2*-weighted GRE scans

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

1st International conference on Medical Imaging with Deep Learning (MIDL), July 2018 (conference)

link (url) [BibTex]

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)

link (url) [BibTex]

Intrinsic disentanglement: an invariance view for deep generative models

Besserve, M., Sun, R., Schölkopf, B.

Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)

PDF [BibTex]

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)

PDF [BibTex]

Tempered Adversarial Networks

Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.

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)

arXiv link (url) Project Page [BibTex]

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)

arXiv link (url) Project Page [BibTex]

Learning Independent Causal Mechanisms

Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Schölkopf, B.

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)

link (url) Project Page [BibTex]

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)

link (url) Project Page [BibTex]

Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)

PDF [BibTex]

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)

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Differentially Private Database Release via Kernel Mean Embeddings

Balog, M., Tolstikhin, I., Schölkopf, B.

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)

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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)

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On Matching Pursuit and Coordinate Descent

Locatello, F., Raj, A., Praneeth Karimireddy, S., Rätsch, G., Schölkopf, B., Stich, S. U., Jaggi, M.

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)

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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)

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Iterative Model-Fitting and Local Controller Optimization - Towards a Better Understanding of Convergence Properties

Wüthrich, M., Schölkopf, B.

Workshop on Prediction and Generative Modeling in Reinforcement Learning at ICML, July 2018 (conference)

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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)

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Wasserstein Auto-Encoders

Tolstikhin, I., Bousquet, O., Gelly, S., Schölkopf, B.

6th International Conference on Learning Representations (ICLR), May 2018 (conference)

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Tolstikhin, I., Bousquet, O., Gelly, S., Schölkopf, B. Wasserstein Auto-Encoders 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

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Fidelity-Weighted Learning

Dehghani, M., Mehrjou, A., Gouws, S., Kamps, J., Schölkopf, B.

6th International Conference on Learning Representations (ICLR), May 2018 (conference)

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Dehghani, M., Mehrjou, A., Gouws, S., Kamps, J., Schölkopf, B. Fidelity-Weighted Learning 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

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Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders

Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.

Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

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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)

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Tempered Adversarial Networks

Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.

Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

arXiv [BibTex]

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)

arXiv [BibTex]

Learning Disentangled Representations with Wasserstein Auto-Encoders

Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.

Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)

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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)

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Automatic Estimation of Modulation Transfer Functions

Bauer, M., Volchkov, V., Hirsch, M., Schölkopf, B.

IEEE International Conference on Computational Photography (ICCP), May 2018 (conference)

DOI [BibTex]

Bauer, M., Volchkov, V., Hirsch, M., Schölkopf, B. Automatic Estimation of Modulation Transfer Functions IEEE International Conference on Computational Photography (ICCP), May 2018 (conference)

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Group invariance principles for causal generative models

Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D.

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)

link (url) [BibTex]

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)

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Cause-Effect Inference by Comparing Regression Errors

Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.

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)

link (url) [BibTex]

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)

link (url) [BibTex]

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.

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)

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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)

DOI Project Page Project Page [BibTex]