166 results (BibTeX)

2012


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Kernels for identifying patterns in datasets containing noise or transformation invariances

Schölkopf, B., Chapelle, C.

United States Patent, No. 8209269, June 2012 (patent)

[BibTex]

2012


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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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On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples

Ben-David, S., Urner, R.

In Algorithmic Learning Theory - 23rd International Conference, 7568, pages: 139-153, Lecture Notes in Computer Science, (Editors: Bshouty, NH. and Stoltz, G and Vayatis, N and Zeugmann, T), Springer Berlin Heidelberg, ALT, 2012 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Domain Adaptation–Can Quantity compensate for Quality?

Ben-David, S., Shalev-Shwartz, S., Urner, R.

In International Symposium on Artificial Intelligence and Mathematics, ISAIM, 2012 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Learning from Weak Teachers

Urner, R., Ben-David, S., Shamir, O.

In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, 22, pages: 1252-1260, (Editors: Lawrence, N. and Girolami, M.), JMLR, AISTATS, 2012 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

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]


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Simultaneous small animal PET/MR reveals different brain networks during stimulation and rest

Wehrl, H., Hossain, M., Lankes, K., Liu, C., Bezrukov, I., Martirosian, P., Reischl, G., Schick, F., Pichler, B.

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

[BibTex]

[BibTex]


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On empirical tradeoffs in large scale hierarchical classification

Babbar, R., Partalas, I., Gaussier, E., Amblard, C.

In 21st ACM International Conference on Information and Knowledge Management, pages: 2299-2302, (Editors: G Lebanon and H Wang and MJ Zaki), ACM, CIKM, 2012 (inproceedings)

Web [BibTex]

Web [BibTex]


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Image denoising: Can plain Neural Networks compete with BM3D?

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

In pages: 2392 - 2399, 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012 (inproceedings)

Abstract
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches. While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well.

PDF Web DOI Project Page [BibTex]

PDF Web DOI Project Page [BibTex]


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Kernel Topic Models

Hennig, P., Stern, D., Herbrich, R., Graepel, T.

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

Abstract
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


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An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli

Hill, N., Schölkopf, B.

Journal of Neural Engineering, 9(2):026011, February 2012 (article)

Abstract
We report on the development and online testing of an electroencephalogram-based brain–computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare 'oddball' stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

PDF DOI Project Page [BibTex]


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ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections

Karaletsos, T., Stegle, O., Dreyer, C., Winn, J., Borgwardt, K.

Bioinformatics, 28(7):1001-1008, April 2012 (article)

Abstract
Motivation: Accurate large-scale phenotyping has recently gained considerable importance in biology. For example, in genome wide association studies technological advances have rendered genotyping cheap, leaving phenotype acquisition as the major bottleneck. Automatic image analysis is one major strategy to phenotype individuals in large numbers. Current approaches for visual phenotyping focus predominantly on summarizing statistics and geometric measures, such as height and width of an individual, or color histograms and patterns. However, more subtle, but biologically informative phenotypes, such as the local deformation of the shape of an individual with respect to the population mean cannot be automatically extracted and quantified by current techniques. Results: We propose a probabilistic machine learning model that allows for the extraction of deformation phenotypes from biological images, making them available as quantitative traits for downstream analysis. Our approach jointly models a collection of images using a learned common template that is mapped onto each image through a deformable smooth transformation. In a case study we analyze the shape deformations of 388 guppy fish (Poecilia reticulata). We find that the flexible shape phenotypes our model extracts are complementary to basic geometric measures. Moreover, these quantitative traits assort the observations into distinct groups and can be mapped to polymorphic genetic loci of the sample set.

Web DOI [BibTex]

Web DOI [BibTex]


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A Kernel Two-Sample Test

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.

Journal of Machine Learning Research, 13, pages: 723-773, March 2012 (article)

Abstract
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD). We present two distribution-free tests based on large deviation bounds for the MMD, and a third test based on the asymptotic distribution of this statistic. The MMD can be computed in quadratic time, although efficient linear time approximations are available. Our statistic is an instance of an integral probability metric, and various classical metrics on distributions are obtained when alternative function classes are used in place of an RKHS. We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


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Bayesian flexible fitting of biomolecular structures into EM maps

Habeck, M.

Biophysical journal, 2012 (article) Submitted

[BibTex]

[BibTex]


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Influence Maximization in Continuous Time Diffusion Networks

Gomez Rodriguez, M., Schölkopf, B.

In Proceedings of the 29th International Conference on Machine Learning, pages: 313-320, (Editors: J, Langford and J, Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Web Project Page [BibTex]

Web Project Page [BibTex]


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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Structured Apprenticeship Learning

Boularias, A., Kroemer, O., Peters, J.

In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (inproceedings)

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


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Technical performance evaluation of a human brain PET/MRI system

Kolb, A., Wehrl, H., Hofmann, M., Judenhofer, M., Eriksson, L., Ladebeck, R., Lichy, M., Byars, L., Michel, C., Schlemmer, H., Schmand, M., Claussen, C., Sossi, V., Pichler, B.

European Radiology, 22(8):1776-1788, March 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Identifying endogenous rhythmic spatio-temporal patterns in micro-electrode array recordings

Besserve, M., Panagiotaropoulos, T., Crocker, B., Kapoor, V., Tolias, A., Panzeri, S., Logothetis, N.

9th annual Computational and Systems Neuroscience meeting (Cosyne), 2012 (poster)

[BibTex]

[BibTex]


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Blind Correction of Optical Aberrations

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

In Computer Vision - ECCV 2012, LNCS Vol. 7574, pages: 187-200, (Editors: A Fitzgibbon, S Lazebnik, P Perona, Y Sato, and C Schmid), Springer, Berlin, Germany, 12th IEEE European Conference on Computer Vision, ECCV, 2012 (inproceedings)

Abstract
Camera lenses are a critical component of optical imaging systems, and lens imperfections compromise image quality. While traditionally, sophisticated lens design and quality control aim at limiting optical aberrations, recent works [1,2,3] promote the correction of optical flaws by computational means. These approaches rely on elaborate measurement procedures to characterize an optical system, and perform image correction by non-blind deconvolution. In this paper, we present a method that utilizes physically plausible assumptions to estimate non-stationary lens aberrations blindly, and thus can correct images without knowledge of specifics of camera and lens. The blur estimation features a novel preconditioning step that enables fast deconvolution. We obtain results that are competitive with state-of-the-art non-blind approaches.

PDF Web DOI Project Page [BibTex]

PDF Web DOI Project Page [BibTex]


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Interactive Domain Adaptation Technique for the Classification of Remote Sensing Images

Persello, C., Dinuzzo, F.

In IEEE International Geoscience and Remote Sensing Symposium , pages: 6872-6875, IEEE, IGARSS, 2012 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Point Cloud Completion Using Symmetries and Extrusions

Kroemer, O., Ben Amor, H., Ewerton, M., Peters, J.

In IEEE-RAS International Conference on Humanoid Robots , pages: 680-685, IEEE, HUMANOIDS, 2012 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Reconstruction using Gaussian mixture models

Joubert, P., Habeck, M.

2012 Gordon Research Conference on Three-Dimensional Electron Microscopy (3DEM), 2012 (poster)

Web [BibTex]

Web [BibTex]


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Support Measure Machines for Quasar Target Selection

Muandet, K.

Astro Imaging Workshop, 2012 (talk)

Abstract
In this talk I will discuss the problem of quasar target selection. The objects attributes in astronomy such as fluxes are often subjected to substantial and heterogeneous measurement uncertainties, especially for the medium-redshift between 2.2 and 3.5 quasars which is relatively rare and must be targeted down to g ~ 22 mag. Most of the previous works for quasar target selection includes UV-excess, kernel density estimation, a likelihood approach, and artificial neural network cannot directly deal with the heterogeneous input uncertainties. Recently, extreme deconvolution (XD) has been used to tackle this problem in a well-posed manner. In this work, we present a discriminative approach for quasar target selection that can deal with input uncertainties directly. To do so, we represent each object as a Gaussian distribution whose mean is the object's attribute vector and covariance is the given flux measurement uncertainty. Given a training set of Gaussian distributions, the support measure machines (SMMs) algorithm are trained and used to build the quasar targeting catalog. Preliminary results will also be presented. Joint work with Jo Bovy and Bernhard Sch{\"o}lkopf

Web [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 (MNRAS), 2012 (article)

[BibTex]

[BibTex]


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A non-monotonic method for large-scale non-negative least squares

Kim, D., Sra, S., Dhillon, I. S.

Optimization Methods and Software, 28(5):1012-1039, Febuary 2012 (article)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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The representer theorem for Hilbert spaces: a necessary and sufficient condition

Dinuzzo, F., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 189-196, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics

Seldin, Y.

Workshop on Statistical Physics of Inference and Control Theory, 2012 (talk)

Web [BibTex]

Web [BibTex]


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LMM-Lasso: A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction

Rakitsch, B., Lippert, C., Stegle, O., Borgwardt, KM.

Bioinformatics, 29(2):206-214, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Complexity of computing distances between geometric trees

Feragen, A.

In Structural, Syntactic, and Statistical Pattern Recognition, LNCS vol 7626, pages: 89-97, (Editors: G Gimel´farb and E Hancock and A Imiya and A Kuijper and M Kudo and S Omachi and T Windeatt and K Yamada), Springer, 14th international workshop on Syntactical and Structural Pattern Recognition (S+SSPR), 2012 (inproceedings)

[BibTex]

[BibTex]


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

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

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

[BibTex]

[BibTex]


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Inferential structure determination from NMR data

Habeck, M.

In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

[BibTex]

[BibTex]


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Same, same, but different: EEG correlates of n-back and span working memory tasks

Scharinger, C., Cienak, G., Walter, C., Zander, TO., Gerjets, P.

In Proceedings of the 48th Congress of the German Society for Psychology, 2012 (inproceedings)

[BibTex]

[BibTex]


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Support Vector Machines, Support Measure Machines, and Quasar Target Selection

Muandet, K.

Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

[BibTex]

[BibTex]


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Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Web [BibTex]


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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]

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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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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Adaptive Classifier Selection in Large-Scale Hierarchical Classification

Partalas, I., Babbar, R., Gaussier, E., Amblard, C.

In Neural Information Processing - 19th International Conference, Lecture Notes in Computer Science, Vol. 7665, pages: 612-619, (Editors: T Huang and Z Zeng and C Li and CS Leung), Springer, ICONIP, 2012 (inproceedings)

Web [BibTex]

Web [BibTex]


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Expectation-Maximization methods for solving (PO)MDPs and optimal control problems

Toussaint, M., Storkey, A., Harmeling, S.

In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

PDF [BibTex]

PDF [BibTex]


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Robot Learning

Sigaud, O., Peters, J.

In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

Web [BibTex]

Web [BibTex]


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Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., Krause, A.

ACM Transactions on Knowledge Discovery from Data, 5(4:21), February 2012 (article)

Abstract
Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

Web DOI Project Page [BibTex]

Web DOI Project Page [BibTex]


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PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits

Seldin, Y., Cesa-Bianchi, N., Auer, P., Laviolette, F., Shawe-Taylor, J.

In JMLR Workshop and Conference Proceedings 26, pages: 98-111, JMLR, Cambridge, MA, USA, On-line Trading of Exploration and Exploitation 2, April 2012 (inproceedings)

Abstract
We develop a new tool for data-dependent analysis of the exploration-exploitation trade-off in learning under limited feedback. Our tool is based on two main ingredients. The first ingredient is a new concentration inequality that makes it possible to control the concentration of weighted averages of multiple (possibly uncountably many) simultaneously evolving and interdependent martingales. The second ingredient is an application of this inequality to the exploration-exploitation trade-off via importance weighted sampling. We apply the new tool to the stochastic multiarmed bandit problem, however, the main importance of this paper is the development and understanding of the new tool rather than improvement of existing algorithms for stochastic multiarmed bandits. In the follow-up work we demonstrate that the new tool can improve over state-of-the-art in structurally richer problems, such as stochastic multiarmed bandits with side information (Seldin et al., 2011a).

PDF PDF [BibTex]

PDF PDF [BibTex]


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High Gamma-Power Predicts Performance in Brain-Computer Interfacing

Grosse-Wentrup, M., Schölkopf, B.

(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

Abstract
Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this nding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.

PDF [BibTex]

PDF [BibTex]