125 results (BibTeX)

2013


Fragmentation of Slow Wave Sleep after Onset of Complete Locked-In State

Soekadar, S., Born, J., Birbaumer, N., Bensch, M., Halder, S., Murguialday, A., Gharabaghi, A., Nijboer, F., Schölkopf, B., Martens, S.

Journal of Clinical Sleep Medicine, 9(9):951-953, 2013 (article)

DOI [BibTex]

2013

DOI [BibTex]


Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

[BibTex]

[BibTex]


Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

[BibTex]


Accurate detection of differential RNA processing

Drewe, P., Stegle, O., Hartmann, L., Kahles, A., Bohnert, R., Wachter, A., Borgwardt, K., Rätsch, G.

Nucleic Acids Research, 41(10):5189-5198, 2013 (article)

DOI [BibTex]

DOI [BibTex]


Detecting regulatory gene–environment interactions with unmeasured environmental factors

Fusi, N., Lippert, C., Borgwardt, K., Lawrence, N., Stegle, O.

Bioinformatics, 29(11):1382-1389, 2013 (article)

DOI [BibTex]

DOI [BibTex]


im3shape: a maximum likelihood galaxy shear measurement code for cosmic gravitational lensing

Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S.

Monthly Notices of the Royal Astronomical Society, 434(2):1604-1618, Oxford University Press, 2013 (article)

DOI [BibTex]

DOI [BibTex]


Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era

Hogg, D., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Lang, D., Montet, B., Schiminovich, D., Schölkopf, B.

arXiv:1309.0653, 2013 (techreport)

link (url) [BibTex]

link (url) [BibTex]


Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars

Montet, B., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Hogg, D., Lang, D., Schiminovich, D., Schölkopf, B.

arXiv:1309.0654, 2013 (techreport)

link (url) [BibTex]

link (url) [BibTex]


PAC-Bayes-Empirical-Bernstein Inequality

Tolstikhin, I., Seldin, Y.

In Advances in Neural Information Processing Systems 26, pages: 109-117, (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)

link (url) [BibTex]

link (url) [BibTex]


On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension

Seldin, Y., Schölkopf, B.

In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

[BibTex]

[BibTex]


PLAL: Cluster-based active learning

Urner, R., Wulff, S., Ben-David, S.

In Proceedings of the 26th Annual Conference on Learning Theory, 30, pages: 376-397, (Editors: Shalev-Shwartz, S. and Steinwart, I.), JMLR, COLT, 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


Monochromatic Bi-Clustering

Wulff, S., Urner, R., Ben-David, S.

In Proceedings of the 30th International Conference on Machine Learning, 28, pages: 145-153, (Editors: Dasgupta, S. and McAllester, D.), JMLR, ICML, 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


Generative Multiple-Instance Learning Models For Quantitative Electromyography

Adel, T., Smith, B., Urner, R., Stashuk, D., Lizotte, D.

In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, AUAI Press, UAI, 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

Abstract
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

website+code pdf link (url) Project Page [BibTex]

website+code pdf link (url) Project Page [BibTex]


How the result of graph clustering methods depends on the construction of the graph

Maier, M., von Luxburg, U., Hein, M.

ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)

Abstract
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one rst has to construct a graph on the data points and then apply a graph clustering algorithm to nd a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) in uences the outcome of the nal clustering result. To this end we study the convergence of cluster quality measures such as the normalized cut or the Cheeger cut on various kinds of random geometric graphs as the sample size tends to in nity. It turns out that the limit values of the same objective function are systematically di erent on di erent types of graphs. This implies that clustering results systematically depend on the graph and can be very di erent for di erent types of graph. We provide examples to illustrate the implications on spectral clustering.

PDF DOI [BibTex]

PDF DOI [BibTex]


What can neurons do for their brain? Communicate selectivity with bursts

Balduzzi, D., Tononi, G.

Theory in Biosciences , 132(1):27-39, Springer, March 2013 (article)

Abstract
Neurons deep in cortex interact with the environment extremely indirectly; the spikes they receive and produce are pre- and post-processed by millions of other neurons. This paper proposes two information-theoretic constraints guiding the production of spikes, that help ensure bursting activity deep in cortex relates meaningfully to events in the environment. First, neurons should emphasize selective responses with bursts. Second, neurons should propagate selective inputs by burst-firing in response to them. We show the constraints are necessary for bursts to dominate information-transfer within cortex, thereby providing a substrate allowing neurons to distribute credit amongst themselves. Finally, since synaptic plasticity degrades the ability of neurons to burst selectively, we argue that homeostatic regulation of synaptic weights is necessary, and that it is best performed offline during sleep.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


Apprenticeship Learning with Few Examples

Boularias, A., Chaib-draa, B.

Neurocomputing, 104, pages: 83-96, March 2013 (article)

Abstract
We consider the problem of imitation learning when the examples, provided by an expert human, are scarce. Apprenticeship learning via inverse reinforcement learning provides an efficient tool for generalizing the examples, based on the assumption that the expert's policy maximizes a value function, which is a linear combination of state and action features. Most apprenticeship learning algorithms use only simple empirical averages of the features in the demonstrations as a statistics of the expert's policy. However, this method is efficient only when the number of examples is sufficiently large to cover most of the states, or the dynamics of the system is nearly deterministic. In this paper, we show that the quality of the learned policies is sensitive to the error in estimating the averages of the features when the dynamics of the system is stochastic. To reduce this error, we introduce two new approaches for bootstrapping the demonstrations by assuming that the expert is near-optimal and the dynamics of the system is known. In the first approach, the expert's examples are used to learn a reward function and to generate furthermore examples from the corresponding optimal policy. The second approach uses a transfer technique, known as graph homomorphism, in order to generalize the expert's actions to unvisited regions of the state space. Empirical results on simulated robot navigation problems show that our approach is able to learn sufficiently good policies from a significantly small number of examples.

Web DOI [BibTex]

Web DOI [BibTex]


A neural population model for visual pattern detection

Goris, R., Putzeys, T., Wagemans, J., Wichmann, F.

Psychological Review, 120(3):472–496, 2013 (article)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Feedback Error Learning for Rhythmic Motor Primitives

Gopalan, N., Deisenroth, M., Peters, J.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), pages: 1317-1322, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


Gaussian Process Vine Copulas for Multivariate Dependence

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

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(2), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Poster: http://people.tuebingen.mpg.de/dlopez/papers/icml2013_gpvine_poster.pdf (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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)

PDF DOI [BibTex]

PDF DOI [BibTex]


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)

PDF [BibTex]

PDF [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

[BibTex]

[BibTex]


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)

PDF DOI [BibTex]

PDF DOI [BibTex]


Accurate indel prediction using paired-end short reads

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

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

Web DOI [BibTex]

Web DOI [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

PDF [BibTex]

PDF [BibTex]


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)

[BibTex]

[BibTex]


Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

PDF Project Page [BibTex]

PDF Project Page [BibTex]


Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling

Cavusoglu, M., Pohmann, R., Burger, H., Uludag, K.

Magnetic Resonance in Medicine, 69(2):524-530, Febuary 2013 (article)

Abstract
Most experiments assume a global transit delay time with blood flowing from the tagging region to the imaging slice in plug flow without any dispersion of the magnetization. However, because of cardiac pulsation, nonuniform cross-sectional flow profile, and complex vessel networks, the transit delay time is not a single value but follows a distribution. In this study, we explored the regional effects of magnetization dispersion on quantitative perfusion imaging for varying transit times within a very large interval from the direct comparison of pulsed, pseudo-continuous, and dual-coil continuous arterial spin labeling encoding schemes. Longer distances between tagging and imaging region typically used for continuous tagging schemes enhance the regional bias on the quantitative cerebral blood flow measurement causing an underestimation up to 37% when plug flow is assumed as in the standard model.

Web DOI [BibTex]

Web DOI [BibTex]


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)

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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)

Web DOI Project Page [BibTex]

Web DOI Project Page [BibTex]


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)

PDF Web [BibTex]

PDF Web [BibTex]


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)

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


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)

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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)

Web Web DOI [BibTex]

Web Web DOI [BibTex]


Domain Generalization via Invariant Feature Representation

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

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

PDF [BibTex]

PDF [BibTex]


When luminance increment thresholds depend on apparent lightness

Maertens, M., Wichmann, F.

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

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

PDF [BibTex]

PDF [BibTex]


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)

DOI [BibTex]

DOI [BibTex]


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)

PDF [BibTex]

PDF [BibTex]


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)

DOI [BibTex]

DOI [BibTex]