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2014


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Pole Balancing with Apollo

Holger Kaden

Eberhard Karls Universität Tübingen, December 2014 (mastersthesis)

am

[BibTex]

2014


[BibTex]


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Modeling the polygenic architecture of complex traits

Rakitsch, Barbara

Eberhard Karls Universität Tübingen, November 2014 (phdthesis)

ei

[BibTex]

[BibTex]


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Learning Coupling Terms for Obstacle Avoidance

Rai, A.

École polytechnique fédérale de Lausanne, August 2014 (mastersthesis)

am

Project Page [BibTex]

Project Page [BibTex]


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Object Tracking in Depth Images Using Sigma Point Kalman Filters

Issac, J.

Karlsruhe Institute of Technology, July 2014 (mastersthesis)

am

Project Page [BibTex]

Project Page [BibTex]


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Modeling the Human Body in 3D: Data Registration and Human Shape Representation

Tsoli, A.

Brown University, Department of Computer Science, May 2014 (phdthesis)

ps

pdf [BibTex]

pdf [BibTex]


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A Novel Causal Inference Method for Time Series

Shajarisales, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Single-Source Domain Adaptation with Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

ei

[BibTex]

[BibTex]


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Higher-Order Tensors in Diffusion Imaging

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, pages: 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (inbook)

ei

[BibTex]

[BibTex]


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Fuzzy Fibers: Uncertainty in dMRI Tractography

Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G.

In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, pages: 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (inbook)

ei

[BibTex]

[BibTex]


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A global analysis of extreme events and consequences for the terrestrial carbon cycle

Zscheischler, J.

Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

ei

[BibTex]

[BibTex]


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Nonconvex Proximal Splitting with Computational Errors

Sra, S.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (inbook)

ei

[BibTex]

[BibTex]


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Learning objective functions for autonomous motion generation

Kalakrishnan, M.

University of Southern California, University of Southern California, Los Angeles, CA, 2014 (phdthesis)

am

Project Page Project Page [BibTex]

Project Page Project Page [BibTex]


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Development of advanced methods for improving astronomical images

Schmeißer, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

ei

[BibTex]

[BibTex]


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The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection

Lacosse, E.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

ei

[BibTex]

[BibTex]


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Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size

Huang, B.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

ei

[BibTex]

[BibTex]


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Analysis of Distance Functions in Graphs

Alamgir, M.

University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)

ei

[BibTex]

[BibTex]


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Data-driven autonomous manipulation

Pastor, P.

University of Southern California, University of Southern California, Los Angeles, CA, 2014 (phdthesis)

am

Project Page Project Page [BibTex]

Project Page Project Page [BibTex]


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Schalten der Polarität magnetischer Vortexkerne durch eine Zwei-Frequenzen Anregung und mittels direkter Einkopplung eines Stroms

Sproll, M.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), Stuttgart, 2014 (phdthesis)

mms

[BibTex]

[BibTex]


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Addressing of Micro-robot Teams and Non-contact Micro-manipulation

Diller, E., Ye, Z., Giltinan, J., Sitti, M.

In Small-Scale Robotics. From Nano-to-Millimeter-Sized Robotic Systems and Applications, pages: 28-38, Springer Berlin Heidelberg, 2014 (incollection)

pi

Project Page [BibTex]

Project Page [BibTex]


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Vortex-Kern-Korrelation in gekoppelten Systemen

Jüllig, P.

Universität Stuttgart, Stuttgart, 2014 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Robot Learning by Guided Self-Organization

Martius, G., Der, R., Herrmann, J. M.

In Guided Self-Organization: Inception, 9, pages: 223-260, Emergence, Complexity and Computation, Springer Berlin Heidelberg, 2014 (incollection)

al

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Realization of a new Magnetic Scanning X-ray Microscope and Investigation of Landau Structures under Pulsed Field Excitation

Weigand, M.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2014 (phdthesis)

mms

[BibTex]

[BibTex]


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Simulated Annealing

Gall, J.

In Encyclopedia of Computer Vision, pages: 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear (inbook)

ps

[BibTex]

[BibTex]


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Nanoporous Materials for Hydrogen Storage and H2/D2 Isotope Separation

Oh, H.

Universität Stuttgart, Stuttgart, 2014 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]

2006


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Prediction of Protein Function from Networks

Shin, H., Tsuda, K.

In Semi-Supervised Learning, pages: 361-376, Adaptive Computation and Machine Learning, (Editors: Chapelle, O. , B. Schölkopf, A. Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract
In computational biology, it is common to represent domain knowledge using graphs. Frequently there exist multiple graphs for the same set of nodes, representing information from different sources, and no single graph is sufficient to predict class labels of unlabelled nodes reliably. One way to enhance reliability is to integrate multiple graphs, since individual graphs are partly independent and partly complementary to each other for prediction. In this chapter, we describe an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple graphs are formulated into one convex optimization problem. The graph-combining method is applied to functional class prediction of yeast proteins.When compared with individual graphs, the combined graph with optimized weights performs significantly better than any single graph.When compared with the semidefinite programming-based support vector machine (SDP/SVM), it shows comparable accuracy in a remarkably short time. Compared with a combined graph with equal-valued weights, our method could select important graphs without loss of accuracy, which implies the desirable property of integration with selectivity.

ei

Web [BibTex]

2006


Web [BibTex]


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Discrete Regularization

Zhou, D., Schölkopf, B.

In Semi-supervised Learning, pages: 237-250, Adaptive computation and machine learning, (Editors: O Chapelle and B Schölkopf and A Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract
Many real-world machine learning problems are situated on finite discrete sets, including dimensionality reduction, clustering, and transductive inference. A variety of approaches for learning from finite sets has been proposed from different motivations and for different problems. In most of those approaches, a finite set is modeled as a graph, in which the edges encode pairwise relationships among the objects in the set. Consequently many concepts and methods from graph theory are adopted. In particular, the graph Laplacian is widely used. In this chapter we present a systemic framework for learning from a finite set represented as a graph. We develop discrete analogues of a number of differential operators, and then construct a discrete analogue of classical regularization theory based on those discrete differential operators. The graph Laplacian based approaches are special cases of this general discrete regularization framework. An important thing implied in this framework is that we have a wide choices of regularization on graph in addition to the widely-used graph Laplacian based one.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel PCA for Image Compression

Huhle, B.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning

Kuss, M.

Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Combining a Filter Method with SVMs

Lal, T., Chapelle, O., Schölkopf, B.

In Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing, Vol. 207, pages: 439-446, Studies in Fuzziness and Soft Computing ; 207, (Editors: I Guyon and M Nikravesh and S Gunn and LA Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the training sets of the competition data. We choose the data set Arcene as an example to explain the approach step by step. In our view the point of this competition was the construction of a well performing classifier rather than the systematic analysis of a specific approach. This is why our search for the best classifier was only guided by the described methods and that we deviated from the road map at several occasions. All calculations were done with the software Spider [2004].

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Embedded methods

Lal, T., Chapelle, O., Weston, J., Elisseeff, A.

In Feature Extraction: Foundations and Applications, pages: 137-165, Studies in Fuzziness and Soft Computing ; 207, (Editors: Guyon, I. , S. Gunn, M. Nikravesh, L. A. Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Embedded methods are a relatively new approach to feature selection. Unlike filter methods, which do not incorporate learning, and wrapper approaches, which can be used with arbitrary classifiers, in embedded methods the features selection part can not be separated from the learning part. Existing embedded methods are reviewed based on a unifying mathematical framework.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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NONLINEAR OPTICAL PROPERTIES OF CHIRAL LIQUIDS Electric-dipolar pseudoscalars in nonlinear optics

Fischer, P., Champagne, B.

In NON-LINEAR OPTICAL PROPERTIES OF MATTER: FROM MOLECULES TO CONDENSED PHASES, 1, pages: 359-381, Challenges and Advances in Computational Chemistry and Physics, 2006 (incollection)

Abstract
We give all overview of linear and nonlinear optical processes that can be specific to chiral molecules in isotropic media. Specifically, we discuss the pseudoscalars that underlie nonlinear optical activity and chiral frequency conversion processes in fluids. We show that nonlinear optical techniques open entirely new ways of exploring chirality: Sum-frequency-generation (SFG) at second-order and BioCARS at fourth-order arise in the electric-dipole approximation and do not require circularly polarized light to detect chiral molecules in solution. Here the frequency conversion in itself is a measure of chirality. This is in contrast to natural optical activity phenomena which are based on the interference of radiation from induced oscillating electric and magnetic dipoles, and which are observed as a differential response to right and left circularly polarized light. We give examples from our SFG experiments in optically active solutions and show how the application of an additional static electric field to sum-frequency generation allows the absolute configuration of the chiral solute to be determined via all electric-dipolar process. Results from ab initio calculations of the SFG pseudoscalar are presented for a number of chiral molecules

pf

[BibTex]

[BibTex]


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Elektronentheorie der magnetischen EXAFS

Gü\ssmann, M.

Universität Stuttgart, Stuttgart, 2006 (mastersthesis)

mms

[BibTex]

[BibTex]


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Approximate nearest neighbor regression in very high dimensions

Vijayakumar, S., DSouza, A., Schaal, S.

In Nearest-Neighbor Methods in Learning and Vision, pages: 103-142, (Editors: Shakhnarovich, G.;Darrell, T.;Indyk, P.), Cambridge, MA: MIT Press, 2006, clmc (inbook)

am

link (url) [BibTex]

link (url) [BibTex]


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Elektronenspektroskopie an Übergangsmetallclustern

He\ssler, M.

Bayerische Julius-Maximilians-Universität, Würzburg, 2006 (phdthesis)

mms

[BibTex]

[BibTex]


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Hydrogen storage by physisorption on porous materials

Panella, B.

Universität Stuttgart, Stuttgart, 2006 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Theory of magnetic x-ray reflectometry on the Co2Pt7 multilayer system

Martosiswoyo, L.

Universität Stuttgart, Stuttgart, 2006 (mastersthesis)

mms

[BibTex]

[BibTex]


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Magnetischer zirkularer Röntgendichroismus an Übergangsmetalloxiden

Lafkioti, M.

Universität Stuttgart, Stuttgart, 2006 (mastersthesis)

mms

[BibTex]

[BibTex]


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Contributions to the theory of x-ray magnetic dichroism

Dörfler, F.

Universität Stuttgart, Stuttgart, 2006 (mastersthesis)

mms

[BibTex]

[BibTex]