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2013


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A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)

Grosse-Wentrup, M., Schölkopf, B.

In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

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PDF DOI [BibTex]

2013


PDF DOI [BibTex]


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The Randomized Dependence Coefficient

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

In Advances in Neural Information Processing Systems 26, pages: 1-9, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei pn

PDF [BibTex]

PDF [BibTex]


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On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit

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

In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1083-1090, IEEE, CVPR, 2013 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Output Kernel Learning Methods

Dinuzzo, F., Ong, C., Fukumizu, K.

In International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications, ROKS, 2013 (inproceedings)

ei

[BibTex]

[BibTex]


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Alignment-based Transfer Learning for Robot Models

Bocsi, B., Csato, L., Peters, J.

In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), pages: 1-7, 2013 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Accurate indel prediction using paired-end short reads

Grimm, D., Hagmann, J., Koenig, D., Weigel, D., Borgwardt, KM.

BMC Genomics, 14(132), 2013 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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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]

DOI [BibTex]


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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]


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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]


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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]


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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]


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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)

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PDF [BibTex]

PDF [BibTex]


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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 [BibTex]

Web DOI [BibTex]


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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]


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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]


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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 [BibTex]

PDF Web [BibTex]


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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)

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PDF [BibTex]

PDF [BibTex]


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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]


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Domain Generalization via Invariant Feature Representation

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

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

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PDF [BibTex]

PDF [BibTex]


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When luminance increment thresholds depend on apparent lightness

Maertens, M., Wichmann, F.

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

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DOI [BibTex]

DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


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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]


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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)

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PDF [BibTex]

PDF [BibTex]


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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]


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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]


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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)

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DOI [BibTex]

DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


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Quantifying causal influences

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

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

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Web [BibTex]

Web [BibTex]


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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)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


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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 [BibTex]

Web DOI [BibTex]


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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]


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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)

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DOI [BibTex]

DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


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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]


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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 [BibTex]

PDF [BibTex]


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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 [BibTex]

Web [BibTex]


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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)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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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 [BibTex]

PDF [BibTex]


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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)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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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]


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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)

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DOI [BibTex]

DOI [BibTex]


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Probabilistic Model-based Imitation Learning

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

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

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PDF DOI [BibTex]

PDF DOI [BibTex]


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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]


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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)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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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]


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The Randomized Dependence Coefficient

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

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

ei pn

PDF [BibTex]

PDF [BibTex]


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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]


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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]