Schuler, C.
Machine Learning Approaches to Image Deconvolution
University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)
Veiga, F., van Hoof, H., Peters, J., Hermans, T.
Stabilizing Novel Objects by Learning to Predict Tactile Slip
In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 5065-5072, IROS, September 2015 (inproceedings)
Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S.
Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer
PLOS Biology, 13(9):e1002257, September 2015 (article)
Paraschos, A., Rueckert, E., Peters, J., Neumann, G.
Model-Free Probabilistic Movement Primitives for Physical Interaction
In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 2860-2866, IROS, September 2015 (inproceedings)
Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H.
Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills
In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 852-857, IROS, September 2015 (inproceedings)
Manschitz, S., Kober, J., Gienger, M., Peters, J.
Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives
In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 449-455, IROS, September 2015 (inproceedings)
Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J.
Reinforcement Learning vs Human Programming in Tetherball Robot Games
In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 6428-6434, IROS, September 2015 (inproceedings)
Ewerton, M., Maeda, G., Peters, J., Neumann, G.
Learning Motor Skills from Partially Observed Movements Executed at Different Speeds
In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 456-463, IROS, September 2015 (inproceedings)
Janzing, D., Schölkopf, B.
Semi-Supervised Interpolation in an Anticausal Learning Scenario
Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)
Ibarra Chaoul, A., Grosse-Wentrup, M.
Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power?
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)
Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.
Testing the role of luminance edges in White’s illusion with contour adaptation
Journal of Vision, 15(11):1-16, August 2015 (article)
Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.
Retrospective motion correction of magnitude-input MR images
First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)
Charpiat, G., Hofmann, M., Schölkopf, B.
Kernel methods in medical imaging
In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)
Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T.
Retrospective rigid motion correction of undersampled MRI data
23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)
Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J. R.
Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction
23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)
Kiefel, M., Jampani, V., Gehler, P. V.
Permutohedral Lattice CNNs
In ICLR Workshop Track, ICLR, May 2015 (inproceedings)
Loktyushin, A.
Blind Retrospective Motion Correction of MR Images
University of Tübingen, Germany, May 2015 (phdthesis)
Besserve, M.
Independence of cause and mechanism in brain networks
DALI workshop on Networks: Processes and Causality, April 2015 (talk)
Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.
Blind multirigid retrospective motion correction of MR images
Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)
Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R. W., Resch, K. J.
A quantum advantage for inferring causal structure
Nature Physics, 11(5):414-420, March 2015 (article)
Sra, S.
Positive definite matrices and the S-divergence
Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (article)
Grau-Moya, J, Braun, DA
Adaptive information-theoretic bounded rational decision-making with parametric priors
pages: 1-4, NIPS Workshop on Bounded Optimality and Rational Metareasoning, December 2015 (conference)
Peters, J., Bühlmann, P.
Structural Intervention Distance (SID) for Evaluating Causal Graphs
Neural Computation , 27(3):771-799, 2015 (article)
Zhang, J., Zhang, K.
Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence
Philosophy of Science, Supplementary Volume 2015, 82(5):930-940, 2015 (article)
Foreman-Mackey, D., Hogg, D., Schölkopf, B., Wang, D.
Increasing the sensitivity of Kepler to Earth-like exoplanets
Workshop: 225th American Astronomical Society Meeting 2015 , pages: 105.01D, 2015 (poster)
Peters, J.
On the Intersection Property of Conditional Independence and its Application to Causal Discovery
Journal of Causal Inference, 3(1):97-108, 2015 (article)
Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.
Information-Theoretic Implications of Classical and Quantum Causal Structures
18th Conference on Quantum Information Processing (QIP), 2015 (talk)
Küffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L., Li, G., Fang, L., Mackey, L., Hardiman, O., Cudkowicz, M., Sherman, A., Ertaylan, G., Grosse-Wentrup, M., Hothorn, T., van Ligtenberg, J., Macke, J., Meyer, T., Schölkopf, B., Tran, L., Vaughan, R., Stolovitzky, G., Leitner, M.
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression
Nature Biotechnology, 33, pages: 51-57, 2015 (article)
Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.
Inference of Cause and Effect with Unsupervised Inverse Regression
In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)
Zhang, K., Zhang, J., Schölkopf, B.
Distinguishing Cause from Effect Based on Exogeneity
In Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, pages: 261-271, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)
Hennig, P.
Probabilistic Interpretation of Linear Solvers
SIAM Journal on Optimization, 25(1):234-260, 2015 (article)
Mariti, C., Muscolo, G., Peters, J., Puig, D., Recchiuto, C., Sighieri, C., Solanas, A., von Stryk, O.
Developing biorobotics for veterinary research into cat movements
Journal of Veterinary Behavior: Clinical Applications and Research, 10(3):248-254, 2015 (article)
Huang, B., Zhang, K., Schölkopf, B.
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment
In 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, pages: 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (inproceedings)
Engbert, R., Trukenbrod, H., Barthelmé, S., Wichmann, F.
Spatial statistics and attentional dynamics in scene viewing
Journal of Vision, 15(1):1-17, 2015 (article)
Lopez-Paz, D., Muandet, K., Recht, B.
The Randomized Causation Coefficient
Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)
Kopp, M., Harmeling, S., Schütz, G., Schölkopf, B., Fähnle, M.
Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter
Ultramicroscopy, 148, pages: 115-122, 2015 (article)
Zhang, K., Gong, M., Schölkopf, B.
Multi-Source Domain Adaptation: A Causal View
In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pages: 3150-3157, AAAI Press, AAAI, 2015 (inproceedings)
Weichwald, S., Meyer, T., Özdenizci, O., Schölkopf, B., Ball, T., Grosse-Wentrup, M.
Causal interpretation rules for encoding and decoding models in neuroimaging
NeuroImage, 110, pages: 48–59, 2015 (article)
van Hoof, H., Peters, J., Neumann, G.
Learning of Non-Parametric Control Policies with High-Dimensional State Features
In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 995–1003, (Editors: Lebanon, G. and Vishwanathan, S.V.N. ), JMLR, AISTATS, 2015 (inproceedings)
Schölkopf, B., Muandet, K., Fukumizu, K., Harmeling, S., Peters, J.
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
Statistics and Computing , 25(4):755-766, 2015 (article)
Schölkopf, B.
Artificial intelligence: Learning to see and act
Nature, News & Views, 518(7540):486-487, 2015 (article)
Maertens, M., Wichmann, F., Shapley, R.
Context affects lightness at the level of surfaces
Journal of Vision, 15(1):1-15, 2015 (article)
Lopez-Paz, D., Muandet, K., Schölkopf, B., Tolstikhin, I.
Towards a Learning Theory of Cause-Effect Inference
In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1452–1461, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)
Wang, D., Foreman-Mackey, D., Hogg, D., Schölkopf, B.
Calibrating the pixel-level Kepler imaging data with a causal data-driven model
Workshop: 225th American Astronomical Society Meeting 2015 , pages: 258.08, 2015 (poster)
Wang, C., Liu, C., Roqueiro, D., Grimm, D., Schwab, R., Becker, C., Lanz, C., Weigel, D.
Genome-wide analysis of local chromatin packing in Arabidopsis thaliana
Genome Research, 25(2):246-256, 2015 (article)
Khatami, M., Schmidt-Wilcke, T., Sundgren, P., Abbasloo, A., Schölkopf, B., Schultz, T.
BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease
In 6th International Workshop on Machine Learning in Medical Imaging, 9352, pages: 52-60, Lecture Notes in Computer Science, (Editors: L. Zhou, L. Wang, Q. Wang and Y. Shi), Springer, MLMI, 2015 (inproceedings)
Kpotufe, S., Urner, R., Ben-David, S.
Hierarchical Label Queries with Data-Dependent Partitions
In Proceedings of the 28th Conference on Learning Theory, 40, pages: 1176-1189, (Editors: Grünwald, P. and Hazan, E. and Kale, S. ), JMLR, COLT, 2015 (inproceedings)
Lopes, M., Peters, J., Piater, J., Toussaint, M., Baisero, A., Busch, B., Erkent, O., Kroemer, O., Lioutikov, R., Maeda, G., Mollard, Y., Munzer, T., Shukla, D.
Semi-Autonomous 3rd-Hand Robot
In Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, 2015 (inproceedings)
Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.
Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI
World Molecular Imaging Conference, 2015 (talk)
O’Donnell, L. J., Schultz, T.
Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data
In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)