2369 results (BibTeX)

2016


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Learning Causal Interaction Network of Multivariate Hawkes Processes

Etesami, S., Kiyavash, N., Zhang, K., Singhal, K.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016, poster presentation (conference)

[BibTex]

2016

[BibTex]


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Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., Zhang, K.

Applied Informatics, 3(3):1-28, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

Weichwald, S., Gretton, A., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 6th International Workshop on Pattern Recognition in NeuroImaging (PRNI 2016), 2016 (conference)

PDF Arxiv Code DOI [BibTex]

PDF Arxiv Code DOI [BibTex]


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Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Townsend, J., Koep, N., Weichwald, S.

Journal of Machine Learning Research, 17(137):1-5, 2016 (article)

PDF Arxiv Code Project page link (url) [BibTex]


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Domain Adaptation with Conditional Transferable Components

Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., Schölkopf, B.

Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), 48, pages: 2839-2848, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M.-F. and Weinberger, K. Q.), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Special Issue on Causal Discovery and Inference

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.

ACM Transactions on Intelligent Systems and Technology (TIST), 7(2), January 2016, (Guest Editors) (misc)

[BibTex]

[BibTex]


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Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

pdf [BibTex]


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An Improved Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M. R., Fomina, T., Jayaram, V., Förster, C., Just, J., M., S., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Proceedings of the Sixth International BCI Meeting, pages: 44, (Editors: Müller-Putz, G. R. and Huggins, J. E. and Steyrl, D.), BCI, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


Thumb xl 2016 peer grading
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

Proceedings of the 3rd ACM conference on Learning @ Scale, pages: 369-378, (Editors: Haywood, J. and Aleven, V. and Kay, J. and Roll, I.), ACM, L@S, 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (conference)

Arxiv Peer-Grading dataset request [BibTex]

Arxiv Peer-Grading dataset request [BibTex]


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Unifying distillation and privileged information

Lopez-Paz, D., Schölkopf, B., Bottou, L., Vapnik, V.

International Conference on Learning Representations, ICLR, 2016 (conference)

Arxiv [BibTex]

Arxiv [BibTex]


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A Population Based Gaussian Mixture Model Incorporating 18F-FDG-PET and DW-MRI Quantifies Tumor Tissue Classes

Divine, M. R., Katiyar, P., Kohlhofer, U., Quintanilla-Martinez, L., Disselhorst, J. A., Pichler, B. J.

Journal of Nuclear Medicine, 57(3):473-479, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning

Büchler, D., Ott, H., Peters, J.

Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, pages: 4086-4092, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (conference)

ICRA16final DOI [BibTex]

ICRA16final DOI [BibTex]


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Causal inference using invariant prediction: identification and confidence intervals

Peters, J., Bühlmann, P., Meinshausen, N.

Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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MERLiN: Mixture Effect Recovery in Linear Networks

Weichwald, S., Grosse-Wentrup, M., Gretton, A.

IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (article)

Arxiv Code PDF DOI [BibTex]

Arxiv Code PDF DOI [BibTex]


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A Scalable Mixed-norm Approach for Learning Lightweight Models in Large-scale Classification

Babbar, R., Muandet, K., Schölkopf, B.

Proceedings of the 2016 SIAM International Conference on Data Mining, pages: 234-242, (Editors: Sanjay Chawla Venkatasubramanian and Wagner Meira Jr.), SDM, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Transfer Learning in Brain-Computer Interfaces

Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.

IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Learning to Deblur

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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Learning Taxonomy Adaptation in Large-scale Classification

Babbar, R., Partalas, I., Gaussier, E., Amini, M., Amblard, C.

Journal of Machine Learning Research, 17(98):1-37, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


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Modeling Confounding by Half-Sibling Regression

Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C., Peters, J.

Proceedings of the National Academy of Science, 113(27):7391-7398, 2016 (article)

Code link (url) DOI [BibTex]

Code link (url) DOI [BibTex]


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On estimation of functional causal models: General results and application to post-nonlinear causal model

Zhang, K., Wang, Z., Zhang, J., Schölkopf, B.

ACM Transactions on Intelligent Systems and Technologies, 7(2), January 2016 (article)

PDF DOI [BibTex]

PDF DOI [BibTex]

2015


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Particle Gibbs for Infinite Hidden Markov Models

Tripuraneni*, N., Gu*, S., Ge, H., Ghahramani, Z.

Advances in Neural Information Processing Systems 28, pages: 2395-2403, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015, *equal contribution (conference)

PDF [BibTex]

2015

PDF [BibTex]


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Neural Adaptive Sequential Monte Carlo

Gu, S., Ghahramani, Z., Turner, R. E.

Advances in Neural Information Processing Systems 28, pages: 2629-2637, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

PDF Supplementary [BibTex]

PDF Supplementary [BibTex]


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Practical Probabilistic Programming with Monads

Ścibior, A., Ghahramani, Z., Gordon, A. D.

Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell, pages: 165-176, Haskell ’15, ACM, 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Subspace Alignement based Domain Adaptation for RCNN detector

Raj, A., V., N., Tuytelaars, T.

Proceedings of the 26th British Machine Vision Conference (BMVC 2015), pages: 166.1-166.11, (Editors: Xianghua Xie and Mark W. Jones and Gary K. L. Tam), 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies

Grimm, Dominik

Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

[BibTex]

[BibTex]


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Quantifying changes in climate variability and extremes: Pitfalls and their overcoming

Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D.

Geophysical Research Letters, 42(22):9990-9998, November 2015 (article)

DOI [BibTex]

DOI [BibTex]


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BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

Rothenhäusler, D., Heinze, C., Peters, J., Meinshausen, N.

Advances in Neural Information Processing Systems 28, pages: 1513-1521, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

DOI [BibTex]


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The effect of frowning on attention

Ibarra Chaoul, A.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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A quantum advantage for inferring causal structure

Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R. W., Resch, K. J.

Nature Physics, 11(5):414-420, March 2015 (article)

Abstract
The problem of inferring causal relations from observed correlations is relevant to a wide variety of scientific disciplines. Yet given the correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both. Only a randomized trial can settle the issue. Here we consider the problem of causal inference for quantum variables. We show that the analogue of a randomized trial, causal tomography, yields a complete solution. We also show that, in contrast to the classical case, one can sometimes infer the causal structure from observations alone. We implement a quantum-optical experiment wherein we control the causal relation between two optical modes, and two measurement schemes—with and without randomization—that extract this relation from the observed correlations. Our results show that entanglement and quantum coherence provide an advantage for causal inference.

DOI [BibTex]

DOI [BibTex]


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Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J. R.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

[BibTex]

[BibTex]


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Retrospective rigid motion correction of undersampled MRI data

Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

[BibTex]

[BibTex]


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Retrospective motion correction of magnitude-input MR images

Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.

International Conference on Machine Learning (ICML) 2015, Workshop on Machine Learning meets Medical Imaging, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, First International Workshop, MLMMI, July 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Testing the role of luminance edges in White’s illusion with contour adaptation

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.

Journal of Vision, 15(11):1-16, August 2015 (article)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.

Journal of Vision, 15(14):1-17, October 2015 (article)

DOI Project Page [BibTex]


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Semi-Supervised Interpolation in an Anticausal Learning Scenario

Janzing, D., Schölkopf, B.

Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power?

Ibarra Chaoul, A., Grosse-Wentrup, M.

Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Causal Discovery Beyond Conditional Independences

Sgouritsa, E.

University of Tübingen, Germany, October 2015 (phdthesis)

[BibTex]

[BibTex]