Header logo is de


2013


no image
Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method

Chen, Z., Zhang, K., Chan, L.

In 13th International Conference on Data Mining, pages: 1003-1008, (Editors: H. Xiong, G. Karypis, B. M. Thuraisingham, D. J. Cook and X. Wu), IEEE Computer Society, ICDM, 2013 (inproceedings)

ei

DOI [BibTex]

2013


DOI [BibTex]


no image
A probabilistic approach to robot trajectory generation

Paraschos, A., Neumann, G., Peters, J.

In Proceedings of the 13th IEEE International Conference on Humanoid Robots (HUMANOIDS), pages: 477-483, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Geometric optimisation on positive definite matrices for elliptically contoured distributions

Sra, S., Hosseini, R.

In Advances in Neural Information Processing Systems 26, pages: 2562-2570, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC

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

43rd Annual Meeting of the Society for Neuroscience (Neuroscience), 2013 (poster)

ei

[BibTex]

[BibTex]


no image
Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D., Chickering, D., Portugualy, E., Ray, D., Simard, P., Snelson, E.

Journal of Machine Learning Research, 14, pages: 3207-3260, 2013 (article)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


no image
Fast Probabilistic Optimization from Noisy Gradients

Hennig, P.

In Proceedings of The 30th International Conference on Machine Learning, JMLR W&CP 28(1), pages: 62–70, (Editors: S Dasgupta and D McAllester), ICML, 2013 (inproceedings)

ei pn

PDF [BibTex]

PDF [BibTex]


no image
Structure and Dynamics of Information Pathways in On-line Media

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In 6th ACM International Conference on Web Search and Data Mining (WSDM), pages: 23-32, (Editors: S Leonardi, A Panconesi, P Ferragina, and A Gionis), ACM, WSDM, 2013 (inproceedings)

ei

Web DOI Project Page [BibTex]

Web DOI Project Page [BibTex]


no image
Evaluation and Analysis of the Performance of the EXP3 Algorithm in Stochastic Environments

Seldin, Y., Szepesvári, C., Auer, P., Abbasi-Yadkori, Y.

In Proceedings of the Tenth European Workshop on Reinforcement Learning , pages: 103-116, (Editors: MP Deisenroth and C Szepesvári and J Peters), JMLR, EWRL, 2013 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Gaussian Process Vine Copulas for Multivariate Dependence

Lopez-Paz, D., Hernandez-Lobato, J., Ghahramani, Z.

International Conference on Machine Learning (ICML), 2013 (poster)

ei

PDF [BibTex]

PDF [BibTex]


no image
Domain adaptation under Target and Conditional Shift

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

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

ei

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


no image
From Ordinary Differential Equations to Structural Causal Models: the deterministic case

Mooij, J., Janzing, D., Schölkopf, B.

In Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence, pages: 440-448, (Editors: A Nicholson and P Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A machine learning approach for non-blind image deconvolution

Schuler, C., Burger, H., Harmeling, S., Schölkopf, B.

In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1067-1074, IEEE, CVPR, 2013 (inproceedings)

ei

Web Web DOI [BibTex]

Web Web DOI [BibTex]


no image
Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

30th International Conference on Machine Learning (ICML2013), 2013 (poster)

ei

PDF [BibTex]

PDF [BibTex]


no image
When luminance increment thresholds depend on apparent lightness

Maertens, M., Wichmann, F.

Journal of Vision, 13(6):1-11, 2013 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Autonomous Reinforcement Learning with Hierarchical REPS

Daniel, C., Neumann, G., Peters, J.

In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), pages: 1-8, 2013 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Efficient network-guided multi-locus association mapping with graph cuts

Azencott, C., Grimm, D., Sugiyama, M., Kawahara, Y., Borgwardt, K.

Bioinformatics, 29(13):i171-i179, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry

Feragen, A., Petersen, J., Grimm, D., Dirksen, A., Pedersen, JH., Borgwardt, KM., de Bruijne, M.

In Information Processing in Medical Imaging, pages: 171-183, (Editors: JC Gee and S Joshi and KM Pohl and WM Wells and L Zöllei), Springer, Berlin Heidelberg, 23rd International Conference on Information Processing in Medical Imaging (IPMI), 2013, Lecture Notes in Computer Science, Vol. 7017 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
On estimation of functional causal models: Post-nonlinear causal model as an example

Zhang, K., Wang, Z., Schölkopf, B.

In First IEEE ICDM workshop on causal discovery , 2013, Held in conjunction with the 12th IEEE International Conference on Data Mining (ICDM 2013) (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Object Modeling and Segmentation by Robot Interaction with Cluttered Environments

van Hoof, H., Krömer, O., Peters, J.

In Proceedings of the IEEE International Conference on Humanoid Robots (HUMANOIDS), pages: 169-176, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Reflection methods for user-friendly submodular optimization

Jegelka, S., Bach, F., Sra, S.

In Advances in Neural Information Processing Systems 26, pages: 1313-1321, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Semi-supervised learning in causal and anticausal settings

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

ei

DOI [BibTex]

DOI [BibTex]


no image
Analyzing locking of spikes to spatio-temporal patterns in the macaque prefrontal cortex

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

Bernstein Conference, 2013 (poster)

ei

DOI [BibTex]

DOI [BibTex]


no image
Quantifying causal influences

Janzing, D., Balduzzi, D., Grosse-Wentrup, M., Schölkopf, B.

Annals of Statistics, 41(5):2324-2358, 2013 (article)

ei

Web [BibTex]

Web [BibTex]


no image
Probabilistic movement modeling for intention inference in human-robot interaction

Wang, Z., Mülling, K., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J.

International Journal of Robotics Research, 32(7):841-858, 2013 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Blind Retrospective Motion Correction of MR Images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine (MRM), 70(6):1608–1618, 2013 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Modeling fixation locations using spatial point processes

Barthelmé, S., Trukenbrod, H., Engbert, R., Wichmann, F.

Journal of Vision, 13(12):1-34, 2013 (article)

Abstract
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.

ei

Web DOI Project Page [BibTex]

Web DOI Project Page [BibTex]


no image
Tractable large-scale optimization in machine learning

Sra, S.

In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

ei

[BibTex]

[BibTex]


no image
A probabilistic model for secondary structure prediction from protein chemical shifts

Mechelke, M., Habeck, M.

Proteins: Structure, Function, and Bioinformatics, 81(6):984–993, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Climate Extremes and the Carbon Cycle

Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M., Seneviratne, S., Zscheischler, J., Beer, C., Buchmann, N., Frank, D., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., Wattenbach, M.

Nature, 500, pages: 287-295, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Data-Efficient Generalization of Robot Skills with Contextual Policy Search

Kupcsik, A., Deisenroth, M., Peters, J., Neumann, G.

In Proceedings of the 27th National Conference on Artificial Intelligence (AAAI 2013), (Editors: desJardins, M. and Littman, M. L.), AAAI Press, 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

In Proceedings 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 449-458, (Editors: Ann Nicholson and Padhraic Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

ei

PDF Project Page [BibTex]

PDF Project Page [BibTex]


no image
Modeling Information Propagation with Survival Theory

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28 (3), pages: 666-674, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

ei

Web Project Page [BibTex]

Web Project Page [BibTex]


no image
How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data

Grosse-Wentrup, M., Harmeling, S., Zander, T., Hill, J., Schölkopf, B.

In Proceedings of the 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI), pages: 102-105, IEEE Xplore Digital Library, PRNI, 2013 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

Sgouritsa, E., Janzing, D., Peters, J., Schölkopf, B.

In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 556-565, (Editors: A Nicholson and P Smyth), AUAI Press Corvallis, Oregon, USA, UAI, 2013 (inproceedings)

ei

PDF Project Page [BibTex]

PDF Project Page [BibTex]


no image
Improving alpha matting and motion blurred foreground estimation

Köhler, R., Hirsch, M., Schölkopf, B., Harmeling, S.

In IEEE Conference on Image Processing (ICIP), pages: 3446-3450, IEEE, ICIP, 2013 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)

ei

PDF [BibTex]

PDF [BibTex]


no image
Identification of stimulus cues in narrow-band tone-in-noise detection using sparse observer models

Schönfelder, V., Wichmann, F.

Journal of the Acoustical Society of America, 134(1):447-463, 2013 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Probabilistic Model-based Imitation Learning

Englert, P., Paraschos, A., Peters, J., Deisenroth, M.

Adaptive Behavior Journal, 21(5):388-403, 2013 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Towards Robot Skill Learning: From Simple Skills to Table Tennis

Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G.

In Machine Learning and Knowledge Discovery in Databases, Proceedings of the European Conference on Machine Learning, Part III (ECML 2013), LNCS 8190, pages: 627-631, (Editors: Blockeel, H.,Kersting, K., Nijssen, S., and Zelezný, F.), Springer, 2013 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


Thumb xl error vs dt fine
Nonparametric dynamics estimation for time periodic systems

Klenske, E., Zeilinger, M., Schölkopf, B., Hennig, P.

In Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing, pages: 486-493 , 2013 (inproceedings)

ei pn

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
Scalable kernels for graphs with continuous attributes

Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., Borgwardt, KM.

In Advances in Neural Information Processing Systems 26, pages: 216-224, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
The Randomized Dependence Coefficient

Lopez-Paz, D., Hennig, P., Schölkopf, B.

Neural Information Processing Systems (NIPS), 2013 (poster)

ei pn

PDF [BibTex]

PDF [BibTex]


no image
Characterization of different types of sharp-wave ripple signatures in the CA1 of the macaque hippocampus

Ramirez-Villegas, J., Logothetis, N., Besserve, M.

4th German Neurophysiology PhD Meeting Networks, 2013 (poster)

ei

Web [BibTex]

Web [BibTex]


no image
Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity

Schultz, T., Gröschel, S.

In Proceedings of Medical Image Computing and Computer-Assisted Intervention, Part I, pages: 663-670, (Editors: K Mori and I Sakuma and Y Sato and C Barillot and N Navab), Springer, MICCAI, 2013, Lecture Notes in Computer Science, vol. 8149 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Metabolic cost as an organizing principle for cooperative learning

Balduzzi, D., Ortega, P., Besserve, M.

Advances in Complex Systems, 16(02n03):1350012, 2013 (article)

ei

Web DOI Project Page [BibTex]

Web DOI Project Page [BibTex]


no image
Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 (inproceedings)

ei

Web Project Page [BibTex]

Web Project Page [BibTex]


no image
MR-based PET Attenuation Correction for PET/MR Imaging

Bezrukov, I., Mantlik, F., Schmidt, H., Schölkopf, B., Pichler, B.

Seminars in Nuclear Medicine, 43(1):45-59, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
MR-based Attenuation Correction Methods for Improved PET Quantification in Lesions within Bone and Susceptibility Artifact Regions

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Schölkopf, B., Pichler, B.

Journal of Nuclear Medicine, 54(10):1768-1774, 2013 (article)

Abstract
Hybrid PET/MR systems have recently entered clinical practice. Thus, the accuracy of MR-based attenuation correction in simultaneously acquired data can now be investigated. We assessed the accuracy of 4 methods of MR-based attenuation correction in lesions within soft tissue, bone, and MR susceptibility artifacts: 2 segmentation-based methods (SEG1, provided by the manufacturer, and SEG2, a method with atlas-based susceptibility artifact correction); an atlas- and pattern recognition–based method (AT&PR), which also used artifact correction; and a new method combining AT&PR and SEG2 (SEG2wBONE). Methods: Attenuation maps were calculated for the PET/MR datasets of 10 patients acquired on a whole-body PET/MR system, allowing for simultaneous acquisition of PET and MR data. Eighty percent iso-contour volumes of interest were placed on lesions in soft tissue (n = 21), in bone (n = 20), near bone (n = 19), and within or near MR susceptibility artifacts (n = 9). Relative mean volume-of-interest differences were calculated with CT-based attenuation correction as a reference. Results: For soft-tissue lesions, none of the methods revealed a significant difference in PET standardized uptake value relative to CT-based attenuation correction (SEG1, −2.6% ± 5.8%; SEG2, −1.6% ± 4.9%; AT&PR, −4.7% ± 6.5%; SEG2wBONE, 0.2% ± 5.3%). For bone lesions, underestimation of PET standardized uptake values was found for all methods, with minimized error for the atlas-based approaches (SEG1, −16.1% ± 9.7%; SEG2, −11.0% ± 6.7%; AT&PR, −6.6% ± 5.0%; SEG2wBONE, −4.7% ± 4.4%). For lesions near bone, underestimations of lower magnitude were observed (SEG1, −12.0% ± 7.4%; SEG2, −9.2% ± 6.5%; AT&PR, −4.6% ± 7.8%; SEG2wBONE, −4.2% ± 6.2%). For lesions affected by MR susceptibility artifacts, quantification errors could be reduced using the atlas-based artifact correction (SEG1, −54.0% ± 38.4%; SEG2, −15.0% ± 12.2%; AT&PR, −4.1% ± 11.2%; SEG2wBONE, 0.6% ± 11.1%). Conclusion: For soft-tissue lesions, none of the evaluated methods showed statistically significant errors. For bone lesions, significant underestimations of −16% and −11% occurred for methods in which bone tissue was ignored (SEG1 and SEG2). In the present attenuation correction schemes, uncorrected MR susceptibility artifacts typically result in reduced attenuation values, potentially leading to highly reduced PET standardized uptake values, rendering lesions indistinguishable from background. While AT&PR and SEG2wBONE show accurate results in both soft tissue and bone, SEG2wBONE uses a two-step approach for tissue classification, which increases the robustness of prediction and can be applied retrospectively if more precision in bone areas is needed.

ei

Web DOI Project Page Project Page [BibTex]


no image
Learning Sequential Motor Tasks

Daniel, C., Neumann, G., Peters, J.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Learning output kernels for multi-task problems

Dinuzzo, F.

Neurocomputing, 118, pages: 119-126, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]