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2018


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Minimum Information Exchange in Distributed Systems

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

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), Miami, Fl, USA, December 2018 (inproceedings) Accepted

ei ics

arXiv [BibTex]

2018


arXiv [BibTex]


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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)

ei

PDF [BibTex]

PDF [BibTex]


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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, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Wasserstein Auto-Encoders

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

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

ei

link (url) [BibTex]

link (url) [BibTex]


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Fidelity-Weighted Learning

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

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

ei

link (url) [BibTex]

link (url) [BibTex]


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Spatio-temporal Transformer Network for Video Restoration

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

European Conference on Computer Vision (ECCV), 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


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Die kybernetische Revolution

Schölkopf, B.

15-Mar-2018, Süddeutsche Zeitung, 2018 (misc)

ei

link (url) [BibTex]

link (url) [BibTex]


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Photorealistic Video Super Resolution

Pérez-Pellitero, E., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

Computer Vision - 14th Asian Conference on Computer Vision (ACCV), 2018 (conference) Submitted

ei

[BibTex]

[BibTex]


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Functional Programming for Modular Bayesian Inference

Ścibior, A., Kammar, O., Ghahramani, Z.

Proceedings of the ACM on Programming Languages, 2(ICFP):83:1-83:29, ACM, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Yurtsever, A., Fercoq, O., Locatello, F., Cevher, V.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5713-5722, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Enhancing the Accuracy and Fairness of Human Decision Making

Valera, I., Singla, A., Gomez Rodriguez, M.

32th Annual Conference on Neural Information Processing Systems (NIPS), 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


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From Deterministic ODEs to Dynamic Structural Causal Models

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

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

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


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Learning Causality and Causality-Related Learning: Some Recent Progress

Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C.

National Science Review, 5(1):26-29, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Boosting Variational Inference: an Optimization Perspective

Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.

Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 464-472, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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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), 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


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groupICA: Independent component analysis for grouped data

Pfister*, N., Weichwald*, S., Bülmann, P., Schölkopf, B.

2018, *equal contribution (article) Submitted

ei

ArXiv Code Project page PDF [BibTex]

ArXiv Code Project page PDF [BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

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arXiv [BibTex]

arXiv [BibTex]


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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Tucker, G., Bhupatiraju, S., Gu, S., Turner, R., Ghahramani, Z., Levine, S.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5022-5031, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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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, 2018 (conference) Accepted

ei

link (url) [BibTex]

link (url) [BibTex]


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Online optimal trajectory generation for robot table tennis

Koc, O., Maeda, G., Peters, J.

Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

ei

PDF link (url) DOI [BibTex]

PDF link (url) DOI [BibTex]


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Blind Justice: Fairness with Encrypted Sensitive Attributes

Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K., Weller, A.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2635-2644, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Boosting Black Box Variational Inference

Locatello*, F., Dresdner*, G., R., K., Valera, I., Rätsch, G.

32th Annual Conference on Neural Information Processing Systems (NIPS), 2018, *equal contribution (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


Thumb xl 2018 prd
Assessing Generative Models via Precision and Recall

Sajjadi, M. S. M., Bachem, O., Lucic, M., Bousquet, O., Gelly, S.

Workshop on Theoretical Foundations and Applications of Deep Generative Models (TADGM) at the 35th International Conference on Machine Learning (ICML), 2018 (conference)

ei

arXiv [BibTex]

arXiv [BibTex]


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Autofocusing-based phase correction

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

Magnetic Resonance in Medicine, 2018, Epub ahead (article)

ei

DOI [BibTex]

DOI [BibTex]


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Temporal Difference Models: Model-Free Deep RL for Model-Based Control

Pong*, V., Gu*, S., Dalal, M., Levine, S.

6th International Conference on Learning Representations (ICLR), 2018, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Thumb xl 2018 tgan
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, 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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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, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems

Mehrjou, A., Schölkopf, B.

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

ei

PDF [BibTex]

PDF [BibTex]


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Generalized phase locking analysis of electrophysiology data

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N. K., Besserve, M.

7th AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles, 2018 (poster)

ei

link (url) [BibTex]

link (url) [BibTex]


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PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Parmas, P., Rasmussen, C., Peters, J., Doya, K.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4062-4071, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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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), 2018 (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


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Learning Invariances using the Marginal Likelihood

van der Wilk, M., Bauer, M., John, S. T., Hensman, J.

32th Annual Conference on Neural Information Processing Systems (NIPS), 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


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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, 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Eysenbach, B., Gu, S., Ibarz, J., Levine, S.

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

ei

Videos link (url) [BibTex]

Videos link (url) [BibTex]


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Generalized Score Functions for Causal Discovery

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

Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


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Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.

Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Invariant Models for Causal Transfer Learning

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.

Journal of Machine Learning Research, 2018 (article) Accepted

ei

[BibTex]

[BibTex]


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Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning

Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R. E.

2018 (conference) Submitted

ei

ArXiv [BibTex]

ArXiv [BibTex]


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Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

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

32th Annual Conference on Neural Information Processing Systems (NIPS), 2018 (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


Thumb xl 2018 tgan
Tempered Adversarial Networks

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

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

ei

arXiv [BibTex]

arXiv [BibTex]


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MOABB: Trustworthy algorithm benchmarking for BCIs

Jayaram, V., Barachant, A.

Journal of Neural Engineering, 2018 (article) Accepted

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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 2018) , 84, pages: 900-909, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Automatic Estimation of Modulation Transfer Functions

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

International Conference on Computational Photography (ICCP), 2018 (conference) Accepted

ei sf

Project Page [BibTex]

Project Page [BibTex]


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Discriminative Transfer Learning for General Image Restoration

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.

IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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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, 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.

ei

RAL18final link (url) DOI [BibTex]

RAL18final link (url) DOI [BibTex]


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Causal Discovery Using Proxy Variables

Rojas-Carulla, M., Baroni, M., Lopez-Paz, D.

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

ei

link (url) [BibTex]

link (url) [BibTex]