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2012


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Learning from Distributions via Support Measure Machines

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B.

26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (poster)

PDF [BibTex]

2012

PDF [BibTex]


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Existential neuroscience: a functional magnetic resonance imaging investigation of neural responses to reminders of one’s mortality

Quirin, M., Loktyushin, A., Arndt, J., Küstermann, E., Lo, Y., Kuhl, J., Eggert, L.

Social Cognitive and Affective Neuroscience, 7(2):193-198, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Juggling Increases Interhemispheric Brain Connectivity: A Visual and Quantitative dMRI Study.

Schultz, T., Gerber, P., Schmidt-Wilcke, T.

Vision, Modeling and Visualization (VMV), 2012 (poster)

[BibTex]

[BibTex]


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Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

Web [BibTex]

Web [BibTex]


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The geometry and statistics of geometric trees

Feragen, A., Lo, P., de Bruijne, M., Nielsen, M., Lauze, F.

T{\"u}bIt day of bioinformatics, June, 2012 (poster)

[BibTex]

[BibTex]


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Active learning for domain adaptation in the supervised classification of remote sensing images

Persello, C., Bruzzone, L.

IEEE Transactions on Geoscience and Remote Sensing, 50(11):4468-4483, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Reinforcement learning to adjust parametrized motor primitives to new situations

Kober, J., Wilhelm, A., Oztop, E., Peters, J.

Autonomous Robots, 33(4):361-379, 2012 (article)

Abstract
Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.

PDF PDF DOI [BibTex]


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On the Empirical Estimation of Integral Probability Metrics

Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B., Lanckriet, G.

Electronic Journal of Statistics, 6, pages: 1550-1599, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Effect of MR contrast agents on quantitative accuracy of PET in combined whole-body PET/MR imaging

Lois, C., Bezrukov, I., Schmidt, H., Schwenzer, N., Werner, M., Kupferschläger, J., Beyer, T.

European Journal of Nuclear Medicine and Molecular Imaging, 39(11):1756-1766, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Multitask Learning in Computational Biology

Widmer, C., Rätsch, G.

JMLR W\&CP. ICML 2011 Unsupervised and Transfer Learning Workshop, 27, pages: 207-216, 2012 (article)

Abstract
Computational Biology provides a wide range of applications for Multitask Learning (MTL) methods. As the generation of labels often is very costly in the biomedical domain, combining data from different related problems or tasks is a promising strategy to reduce label cost. In this paper, we present two problems from sequence biology, where MTL was successfully applied. For this, we use regularization-based MTL methods, with a special focus on the case of a hierarchical relationship between tasks. Furthermore, we propose strategies to refine the measure of task relatedness, which is of central importance in MTL and finally give some practical guidelines, when MTL strategies are likely to pay off.

PDF [BibTex]

PDF [BibTex]


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Centrality of the Mammalian Functional Brain Network

Besserve, M., Bartels, A., Murayama, Y., Logothetis, N.

42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (poster)

[BibTex]

[BibTex]


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Kernel Mean Embeddings of POMDPs

Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K.

21st Machine Learning Summer School , 2012 (poster)

[BibTex]

[BibTex]


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Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis

Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., Jenkins, DJ., Penfold, CA., Baxter, Ll., Breeze, E., Kiddle, SJ., Rhodes, J., Atwell, S., Kliebenstein, D., Kim, Y-S., Stegle, O., Borgwardt, KM., others

The Plant Cell Online, 24(9):3530-3557, 2012, all authors: Oliver Windram,Priyadharshini Madhou,Stuart McHattie,Claire Hill,Richard Hickman,Emma Cooke,Dafyd J. Jenkins,Christopher A. Penfold,Laura Baxter,Emily Breeze,Steven J. Kiddle,Johanna Rhodes,Susanna Atwell,Daniel J. (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Improved Linear Mixed Models for Genome-Wide Association Studies

Listgarten, J., Lippert, C., Kadie, CM., Davidson, RI., Eskin, E., Heckerman, D.

Nature Methods, 9, pages: 525–526, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Calibration of Boltzmann distribution priors in Bayesian data analysis

Mechelke, M., Habeck, M.

Physical Review E, 86(6):066705, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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CSB: A Python framework for computational structural biology

Kalev, I., Mechelke, M., Kopec, K., Holder, T., Carstens, S., Habeck, M.

Bioinformatics, 28(22):2996-2997, 2012 (article)

Abstract
Summary: Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice. Availability: Stable release packages are available for download from the Python Package Index (PyPI), as well as from the project’s web site http://csb.codeplex.com.

Web DOI [BibTex]

Web DOI [BibTex]


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Semi-Supervised Domain Adaptation with Copulas

Lopez-Paz, D., Hernandez-Lobato, J., Schölkopf, B.

Neural Information Processing Systems (NIPS), 2012 (poster)

PDF [BibTex]

PDF [BibTex]


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Therapy monitoring of patients with chronic sclerodermic graft-versus-host-disease using PET/MRI

Sauter, A., Schmidt, H., Mantlik, F., Kolb, A., Federmann, B., Bethge, W., Reimold, M., Pfannenberg, C., Pichler, B., Horger, M.

2012 SNM Annual Meeting, 2012 (poster)

Web [BibTex]

Web [BibTex]


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Significant global reduction of carbon uptake by water-cycle driven extreme vegetation anomalies

Zscheischler, J., Mahecha, M., von Buttlar, J., Harmeling, S., Jung, M., Randerson, J., Reichstein, M.

Nature Geoscience, 2012 (article) In revision

[BibTex]

[BibTex]


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Mining correlated loci at a genome-wide scale

Velkov, V.

Eberhard Karls Universität Tübingen, Germany, 2012 (mastersthesis)

[BibTex]

[BibTex]


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The PET Performance Measurements of A Next Generation Dedicated Small Animal PET/MR Scanner

Liu, C., Hossain, M., Bezrukov, I., Wehrl, H., Kolb, A., Judenhofer, M., Pichler, B.

World Molecular Imaging Congress (WMIC), 2012 (poster)

[BibTex]

[BibTex]


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Evaluation of Whole-Body MR-Based Attenuation Correction in Bone and Soft Tissue Lesions

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

Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), 2012 (poster)

[BibTex]

[BibTex]


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Design of a Haptic Interface for a Gastrointestinal Endoscopy Simulation

Yu, S., Woo, H. S., Son, H. I., Ahn, W., Jung, H., Lee, D. Y., Yi, S. Y.

Advanced Robotics, 26(18):2115-2143, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Measurement and calibration of noise bias in weak lensing galaxy shape estimation

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

Monthly Notices of the Royal Astronomical Society, 427(4):2711-2722, Oxford University Press, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Image analysis for cosmology: results from the GREAT10 Galaxy Challenge

Kitching, T. D., Balan, S. T., Bridle, S., Cantale, N., Courbin, F., Eifler, T., Gentile, M., Gill, M. S. S., Harmeling, S., Heymans, C., others,

Monthly Notices of the Royal Astronomical Society, 423(4):3163-3208, Oxford University Press, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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First SN Discoveries from the Dark Energy Survey

Abbott, T., Abdalla, F., Achitouv, I., Ahn, E., Aldering, G., Allam, S., Alonso, D., Amara, A., Annis, J., Antonik, M., others,

The Astronomer's Telegram, 4668, pages: 1, 2012 (article)

[BibTex]

[BibTex]


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A sensorimotor paradigm for Bayesian model selection

Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 6(291):1-16, October 2012 (article)

Abstract
Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects’ choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.

DOI [BibTex]

DOI [BibTex]


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Risk-Sensitivity in Bayesian Sensorimotor Integration

Grau-Moya, J, Ortega, PA, Braun, DA

PLoS Computational Biology, 8(9):1-7, sep 2012 (article)

Abstract
Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.

DOI [BibTex]

DOI [BibTex]

2004


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On the representation, learning and transfer of spatio-temporal movement characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, M.

International Journal of Humanoid Robotics, 1(4):613-636, December 2004 (article)

[BibTex]

2004

[BibTex]


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Insect-inspired estimation of egomotion

Franz, MO., Chahl, JS., Krapp, HG.

Neural Computation, 16(11):2245-2260, November 2004 (article)

Abstract
Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

PDF PostScript Web DOI [BibTex]

PDF PostScript Web DOI [BibTex]


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Efficient face detection by a cascaded support-vector machine expansion

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

Proceedings of The Royal Society of London A, 460(2501):3283-3297, A, November 2004 (article)

Abstract
We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Fast Binary and Multi-Output Reduced Set Selection

Weston, J., Bakir, G.

(132), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2004 (techreport)

Abstract
We propose fast algorithms for reducing the number of kernel evaluations in the testing phase for methods such as Support Vector Machines (SVM) and Ridge Regression (RR). For non-sparse methods such as RR this results in significantly improved prediction time. For binary SVMs, which are already sparse in their expansion, the pay off is mainly in the cases of noisy or large-scale problems. However, we then further develop our method for multi-class problems where, after choosing the expansion to find vectors which describe all the hyperplanes jointly, we again achieve significant gains.

PostScript [BibTex]

PostScript [BibTex]


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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W.

(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

PDF [BibTex]

PDF [BibTex]


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S-cones contribute to flicker brightness in human vision

Wehrhahn, C., Hill, NJ., Dillenburger, B.

34(174.12), 34th Annual Meeting of the Society for Neuroscience (Neuroscience), October 2004 (poster)

Abstract
In the retina of primates three cone types sensitive to short, middle and long wavelengths of light convert photons into electrical signals. Many investigators have presented evidence that, in color normal observers, the signals of cones sensitive to short wavelengths of light (S-cones) do not contribute to the perception of brightness of a colored surface when this is alternated with an achromatic reference (flicker brightness). Other studies indicate that humans do use S-cone signals when performing this task. Common to all these studies is the small number of observers, whose performance data are reported. Considerable variability in the occurrence of cone types across observers has been found, but, to our knowledge, no cone counts exist from larger populations of humans. We reinvestigated how much the S-cones contribute to flicker brightness. 76 color normal observers were tested in a simple psychophysical procedure neutral to the cone type occurence (Teufel & Wehrhahn (2000), JOSA A 17: 994 - 1006). The data show that, in the majority of our observers, S-cones provide input with a negative sign - relative to L- and M-cone contribution - in the task in question. There is indeed considerable between-subject variability such that for 20 out of 76 observers the magnitude of this input does not differ significantly from 0. Finally, we argue that the sign of S-cone contribution to flicker brightness perception by an observer cannot be used to infer the relative sign their contributions to the neuronal signals carrying the information leading to the perception of flicker brightness. We conclude that studies which use only a small number of observers may easily fail to find significant evidence for the small but significant population tendency for the S-cones to contribute to flicker brightness. Our results confirm all earlier results and reconcile their contradictory interpretations.

Web [BibTex]

Web [BibTex]


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Learning Motor Primitives with Reinforcement Learning

Peters, J., Schaal, S.

AAAI Fall Symposium on Real-Life Reinforcement Learning 2004, 2004, pages: 1, October 2004 (poster)

Web [BibTex]

Web [BibTex]


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Independent component analysis and beyond

Harmeling, S.

Biologische Kybernetik, Universität Potsdam, Potsdam, October 2004 (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Semi-Supervised Induction

Yu, K., Tresp, V., Zhou, D.

(141), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, August 2004 (techreport)

Abstract
Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

PDF PDF [BibTex]

PDF PDF [BibTex]


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Learning kernels from biological networks by maximizing entropy

Tsuda, K., Noble, W.

Bioinformatics, 20(Suppl. 1):i326-i333, August 2004 (article)

Abstract
Motivation: The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. Results: We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein–protein interaction networks.

PDF Web [BibTex]

PDF Web [BibTex]


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On Hausdorff Distance Measures

Shapiro, MD., Blaschko, MB.

Department of Computer Science, University of Massachusetts Amherst, August 2004 (techreport)

[BibTex]

[BibTex]


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Masking effect produced by Mach bands on the detection of narrow bars of random polarity

Henning, GB., Hoddinott, KT., Wilson-Smith, ZJ., Hill, NJ.

Journal of the Optical Society of America, 21(8):1379-1387, A, August 2004 (article)

[BibTex]

[BibTex]


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Object categorization with SVM: kernels for local features

Eichhorn, J., Chapelle, O.

(137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

PDF [BibTex]

PDF [BibTex]


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Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

(126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and one image classification problem.

PDF [BibTex]

PDF [BibTex]


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Kernels, Associated Structures and Generalizations

Hein, M., Bousquet, O.

(127), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
This paper gives a survey of results in the mathematical literature on positive definite kernels and their associated structures. We concentrate on properties which seem potentially relevant for Machine Learning and try to clarify some results that have been misused in the literature. Moreover we consider different lines of generalizations of positive definite kernels. Namely we deal with operator-valued kernels and present the general framework of Hilbertian subspaces of Schwartz which we use to introduce kernels which are distributions. Finally indefinite kernels and their associated reproducing kernel spaces are considered.

PDF [BibTex]

PDF [BibTex]


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Triangle Fixing Algorithms for the Metric Nearness Problem

Dhillon, I., Sra, S., Tropp, J.

Univ. of Texas at Austin, June 2004 (techreport)

PDF [BibTex]

PDF [BibTex]