Header logo is ei


2019


no image
Convolutional neural networks: A magic bullet for gravitational-wave detection?

Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B.

Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)

link (url) DOI [BibTex]

2019

link (url) DOI [BibTex]


no image
Data scarcity, robustness and extreme multi-label classification

Babbar, R., Schölkopf, B.

Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

DOI [BibTex]

DOI [BibTex]


no image
SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Miladinovic, D., Muheim, C., Bauer, S., Spinnler, A., Noain, D., Bandarabadi, M., Gallusser, B., Krummenacher, G., Baumann, C., Adamantidis, A., Brown, S. A., Buhmann, J. M.

PLOS Computational Biology, 15(4):1-30, Public Library of Science, April 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
A 32-channel multi-coil setup optimized for human brain shimming at 9.4T

Aghaeifar, A., Zhou, J., Heule, R., Tabibian, B., Schölkopf, B., Jia, F., Zaitsev, M., Scheffler, K.

Magnetic Resonance in Medicine, 2019, (Early View) (article)

DOI [BibTex]

DOI [BibTex]


Multidimensional Contrast Limited Adaptive Histogram Equalization
Multidimensional Contrast Limited Adaptive Histogram Equalization

Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R. P.

IEEE Access, 7, pages: 165437-165447, 2019 (article)

arXiv link (url) DOI [BibTex]

arXiv link (url) DOI [BibTex]


no image
TD-regularized actor-critic methods

Parisi, S., Tangkaratt, V., Peters, J., Khan, M. E.

Machine Learning, 108(8):1467-1501, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Tronarp, F., Kersting, H., Särkkä, S. H. P.

Statistics and Computing, 29(6):1297-1315, 2019 (article)

DOI [BibTex]


Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.

2019 (article) Submitted

Abstract
High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

Arxiv Video [BibTex]


no image
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Pfister*, N., Weichwald*, S., Bühlmann, P., Schölkopf, B.

Journal of Machine Learning Research, 20(147):1-50, 2019, *equal contribution (article)

ArXiv Code Project page PDF link (url) Project Page Project Page [BibTex]


no image
Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 116(10):3988-3993, National Academy of Sciences, 2019 (article)

link (url) DOI Project Page Project Page [BibTex]

link (url) DOI Project Page Project Page [BibTex]


no image
Entropic Regularization of Markov Decision Processes

Belousov, B., Peters, J.

Entropy, 21(7):674, 2019 (article)

link (url) DOI [BibTex]


no image
Searchers adjust their eye-movement dynamics to target characteristics in natural scenes

Rothkegel, L., Schütt, H., Trukenbrod, H., Wichmann, F. A., Engbert, R.

Scientific Reports, 9(1635), 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing

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

Journal of Vision, 19(6):19, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Quantum mean embedding of probability distributions

Kübler, J. M., Muandet, K., Schölkopf, B.

Physical Review Research, 1(3):033159, American Physical Society, 2019 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Inferring causation from time series with perspectives in Earth system sciences

Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M., Munoz-Mari, J., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J.

Nature Communications, 10(2553), 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Analysis of cause-effect inference by comparing regression errors

Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.

PeerJ Computer Science, 5, pages: e169, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Learning Intention Aware Online Adaptation of Movement Primitives

Koert, D., Pajarinen, J., Schotschneider, A., Trick, S., Rothkopf, C., Peters, J.

IEEE Robotics and Automation Letters, 4(4):3719-3726, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Spread-spectrum magnetic resonance imaging

Scheffler, K., Loktyushin, A., Bause, J., Aghaeifar, A., Steffen, T., Schölkopf, B.

Magnetic Resonance in Medicine, 82(3):877-885, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
How Cognitive Models of Human Body Experience Might Push Robotics

Schürmann, T., Mohler, B. J., Peters, J., Beckerle, P.

Frontiers in Neurorobotics, 13(14), 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., Helmstaedter, M.

Science, 366(6469):eaay3134, American Association for the Advancement of Science, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, M., Arenz, O., Maeda, G., Koert, D., Kolev, Z., Takahashi, M., Peters, J.

Frontiers in Robotics and AI, 6, pages: 89, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Brainglance: Visualizing Group Level MRI Data at One Glance

Stelzer, J., Lacosse, E., Bause, J., Scheffler, K., Lohmann, G.

Frontiers in Neuroscience, 13(972), 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Klus, S., Schuster, I., Muandet, K.

Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Workshops of the seventh international brain-computer interface meeting: not getting lost in translation

Huggins, J. E., Guger, C., Aarnoutse, E., Allison, B., Anderson, C. W., Bedrick, S., Besio, W., Chavarriaga, R., Collinger, J. L., Do, A. H., Herff, C., Hohmann, M., Kinsella, M., Lee, K., Lotte, F., Müller-Putz, G., Nijholt, A., Pels, E., Peters, B., Putze, F., Rupp, R. S. G., Scott, S., Tangermann, M., Tubig, P., Zander, T.

Brain-Computer Interfaces, 6(3):71-101, Taylor & Francis, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Compatible natural gradient policy search

Pajarinen, J., Thai, H. L., Akrour, R., Peters, J., Neumann, G.

Machine Learning, 108(8):1443-1466, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Learning stable and predictive structures in kinetic systems

Pfister, N., Bauer, S., Peters, J.

Proceedings of the National Academy of Sciences (PNAS), 116(51):25405-25411, 2019 (article)

DOI [BibTex]

DOI [BibTex]


no image
Fairness Constraints: A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez-Rodriguez, M., Krishna, P.

Journal of Machine Learning Research, 20(75):1-42, 2019 (article)

link (url) [BibTex]

link (url) [BibTex]

2004


no image
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]


no image
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]


no image
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]


no image
Pattern detection methods and systems and face detection methods and systems

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

United States Patent, No 6804391, October 2004 (patent)

[BibTex]

[BibTex]


no image
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]


no image
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]


no image
Support Vector Channel Selection in BCI

Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.

IEEE Transactions on Biomedical Engineering, 51(6):1003-1010, June 2004 (article)

Abstract
Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying EEG signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of Support Vector Machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.

DOI [BibTex]

DOI [BibTex]


no image
Distance-Based Classification with Lipschitz Functions

von Luxburg, U., Bousquet, O.

Journal of Machine Learning Research, 5, pages: 669-695, June 2004 (article)

Abstract
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. It will turn out that using Lipschitz functions as decision functions, the inverse of the Lipschitz constant can be interpreted as the size of a margin. In order to construct a clean mathematical setup we isometrically embed the given metric space into a Banach space and the space of Lipschitz functions into its dual space. To analyze the resulting algorithm, we prove several representer theorems. They state that there always exist solutions of the Lipschitz classifier which can be expressed in terms of distance functions to training points. We provide generalization bounds for Lipschitz classifiers in terms of the Rademacher complexities of some Lipschitz function classes. The generality of our approach can be seen from the fact that several well-known algorithms are special cases of the Lipschitz classifier, among them the support vector machine, the linear programming machine, and the 1-nearest neighbor classifier.

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


no image
cDNA-Microarray Technology in Cartilage Research - Functional Genomics of Osteoarthritis [in German]

Aigner, T., Finger, F., Zien, A., Bartnik, E.

Zeitschrift f{\"u}r Orthop{\"a}die und ihre Grenzgebiete, 142(2):241-247, April 2004 (article)

Abstract
Functional genomics represents a new challenging approach in order to analyze complex diseases such as osteoarthritis on a molecular level. The characterization of the molecular changes of the cartilage cells, the chondrocytes, enables a better understanding of the pathomechanisms of the disease. In particular, the identification and characterization of new target molecules for therapeutic intervention is of interest. Also, potential molecular markers for diagnosis and monitoring of osteoarthritis contribute to a more appropriate patient management. The DNA-microarray technology complements (but does not replace) biochemical and biological research in new disease-relevant genes. Large-scale functional genomics will identify molecular networks such as yet identified players in the anabolic-catabolic balance of articular cartilage as well as disease-relevant intracellular signaling cascades so far rather unknown in articular chondrocytes. However, at the moment it is also important to recognize the limitations of the microarray technology in order to avoid over-interpretation of the results. This might lead to misleading results and prevent to a significant extent a proper use of the potential of this technology in the field of osteoarthritis.

[BibTex]

[BibTex]


no image
A Compression Approach to Support Vector Model Selection

von Luxburg, U., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 5, pages: 293-323, April 2004 (article)

Abstract
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct "compression coefficients" for SVMs which measure the amount by which the training labels can be compressed by a code built from the separating hyperplane. The main idea is to relate the coding precision to geometrical concepts such as the width of the margin or the shape of the data in the feature space. The so derived compression coefficients combine well known quantities such as the radius-margin term R^2/rho^2, the eigenvalues of the kernel matrix, and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on benchmark data sets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.

PDF [BibTex]

PDF [BibTex]


no image
Injecting noise for analysing the stability of ICA components

Harmeling, S., Meinecke, F., Müller, K.

Signal Processing, 84(2):255-266, February 2004 (article)

Abstract
Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliability and the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well by our method. In a toy experiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratory data analysis of real-world data.

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Independent component analysis and beyond

Oja, E., Harmeling, S., Almeida, L.

Signal Processing, 84(2):215-216, February 2004 (article)

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Experimentally optimal v in support vector regression for different noise models and parameter settings

Chalimourda, A., Schölkopf, B., Smola, A.

Neural Networks, 17(1):127-141, January 2004 (article)

Abstract
In Support Vector (SV) regression, a parameter ν controls the number of Support Vectors and the number of points that come to lie outside of the so-called var epsilon-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of ν that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression. As a side effect of the experiments, valuable information about the generalization behavior of the remaining SVM parameters and their dependencies is gained. The experimental findings are valid even for complex ‘real-world’ data sets. Based on our results on the role of the ν-SVM parameters, we discuss various model selection methods.

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Protein ranking: from local to global structure in the protein similarity network

Weston, J., Elisseeff, A., Zhou, D., Leslie, C., Noble, W.

Proceedings of the National Academy of Science, 101(17):6559-6563, 2004 (article)

Abstract
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a pre-computed, weighted network. The resulting ranking algorithm, evaluated using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as PSI-BLAST.

Web [BibTex]

Web [BibTex]


no image
Constant infusion H215O PET and acetazolamide challenge in the assessment of the cerebral perfusion status

Weber, B., Westera, G., Treyer, V., Burger, C., Kahn, N., Buck, A.

Journal of Nuclear Medicine, (45):1344-1349, 2004 (article)

[BibTex]

[BibTex]


no image
Asymptotic Properties of the Fisher Kernel

Tsuda, K., Akaho, S., Kawanabe, M., Müller, K.

Neural Computation, 16(1):115-137, 2004 (article)

PDF [BibTex]

PDF [BibTex]


no image
Some observations on the effects of slant and texture type on slant-from-texture

Rosas, P., Wichmann, F., Wagemans, J.

Vision Research, 44(13):1511-1535, 2004 (article)

Abstract
We measure the performance of five subjects in a slant-discrimination task for differently textured planes. As textures we used uniform lattices, randomly displaced lattices, circles (polka dots), Voronoi tessellations, plaids, 1/f noise, “coherent” noise and a leopard skin-like texture. Our results show: (1) Improving performance with larger slants for all textures. (2) Thus, following from (1), cases of “non-symmetrical” performance around a particular orientation. (3) For orientations sufficiently slanted, the different textures do not elicit major differences in performance, (4) while for orientations closer to the vertical plane there are marked differences between them. (5) These differences allow a rank-order of textures to be formed according to their “helpfulness”– that is, how easy the discrimination task is when a particular texture is mapped on the plane. Polka dots tend to allow the best slant discrimination performance, noise patterns the worst. Two additional experiments were conducted to test the generality of the obtained rank-order. First, the tilt of the planes was rotated to break the axis of gravity present in the original discrimination experiment. Second, the task was changed to a slant report task via probe adjustment. The results of both control experiments confirmed the texture-based rank-order previously obtained. We comment on the importance of these results for depth perception research in general, and in particular the implications our results have for studies of cue combination (sensor fusion) using texture as one of the cues involved.

PDF [BibTex]

PDF [BibTex]


no image
Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

Tsuda, K., Uda, S., Kin, T., Asai, K.

Neural Processing Letters, 19, pages: 63-72, 2004 (article)

PDF [BibTex]

PDF [BibTex]


no image
A Tutorial on Support Vector Regression

Smola, A., Schölkopf, B.

Statistics and Computing, 14(3):199-222, 2004 (article)

Web [BibTex]

Web [BibTex]