Header logo is ei


2018


no image
A virtual reality environment for experiments in assistive robotics and neural interfaces

Bustamante, S.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

PDF [BibTex]

2018

PDF [BibTex]


no image
Optimal Trajectory Generation and Learning Control for Robot Table Tennis

Koc, O.

Technical University Darmstadt, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]


no image
Distribution-Dissimilarities in Machine Learning

Simon-Gabriel, C. J.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]


no image
Domain Adaptation Under Causal Assumptions

Lechner, T.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

[BibTex]

[BibTex]


no image
Maschinelles Lernen: Entwicklung ohne Grenzen?

Schökopf, B.

In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

[BibTex]


no image
Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Reinforcement Learning for High-Speed Robotics with Muscular Actuation

Guist, S.

Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

[BibTex]

[BibTex]


no image
Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

[BibTex]

[BibTex]


no image
Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

Project Page [BibTex]

Project Page [BibTex]


no image
Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]


no image
A machine learning approach to taking EEG-based computer interfaces out of the lab

Jayaram, V.

Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]

2017


no image
Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF [BibTex]

2017

PDF [BibTex]


no image
Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S.

In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

Project Page [BibTex]

Project Page [BibTex]


no image
Policy Gradient Methods

Peters, J., Bagnell, J.

In Encyclopedia of Machine Learning and Data Mining, pages: 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Unsupervised clustering of EOG as a viable substitute for optical eye-tracking

Flad, N., Fomina, T., Bülthoff, H. H., Chuang, L. L.

In First Workshop on Eye Tracking and Visualization (ETVIS 2015), pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)

DOI [BibTex]

DOI [BibTex]


no image
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B.

Dagstuhl Reports, 6(11):142-167, 2017 (book)

DOI [BibTex]


no image
Statistical Asymmetries Between Cause and Effect

Janzing, D.

In Time in Physics, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Robot Learning

Peters, J., Tedrake, R., Roy, N., Morimoto, J.

In Encyclopedia of Machine Learning and Data Mining, pages: 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Development and Evaluation of a Portable BCI System for Remote Data Acquisition

Emde, T.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

[BibTex]

[BibTex]


no image
Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis

Fomina, T.

Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)

[BibTex]

[BibTex]


no image
Causal models for decision making via integrative inference

Geiger, P.

University of Stuttgart, Germany, 2017 (phdthesis)

[BibTex]

[BibTex]


no image
Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation

Sücker, K.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

[BibTex]

[BibTex]

2006


no image
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.

Web [BibTex]

2006

Web [BibTex]


no image
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.

PDF Web [BibTex]

PDF Web [BibTex]


no image
Extraction of visual features from natural video data using Slow Feature Analysis

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2006 (diplomathesis)

Abstract
Das Forschungsprojekt NeuRoBot hat das un{\"u}berwachte Erlernen einer neuronal inspirierten Steuerungsarchitektur zum Ziel, und zwar unter den Randbedingungen biologischer Plausibilit{\"a}t und der Benutzung einer Kamera als einzigen Sensor. Visuelle Merkmale, die ein angemessenes Abbild der Umgebung liefern, sind unerl{\"a}sslich, um das Ziel kollisionsfreier Navigation zu erreichen. Zeitliche Koh{\"a}renz ist ein neues Lernprinzip, das in der Lage ist, Erkenntnisse aus der Biologie des Sehens zu reproduzieren. Es wird durch die Beobachtung motiviert, dass die “Sensoren” der Retina auf deutlich k{\"u}rzeren Zeitskalen variieren als eine abstrakte Beschreibung. Zeitliche Langsamkeitsanalyse l{\"o}st das Problem, indem sie zeitlich langsam ver{\"a}nderliche Signale aus schnell ver{\"a}nderlichen Eingabesignalen extrahiert. Eine Verallgemeinerung auf Signale, die nichtlinear von den Eingaben abh{\"a}ngen, ist durch die Anwendung des Kernel-Tricks m{\"o}glich. Das einzig benutzte Vorwissen ist die zeitliche Glattheit der gewonnenen Signale. In der vorliegenden Diplomarbeit wird Langsamkeitsanalyse auf Bildausschnitte von Videos einer Roboterkamera und einer Simulationsumgebung angewendet. Zuallererst werden mittels Parameterexploration und Kreuzvalidierung die langsamst m{\"o}glichen Funktionen bestimmt. Anschließend werden die Merkmalsfunktionen analysiert und einige Ansatzpunkte f{\"u}r ihre Interpretation angegeben. Aufgrund der sehr großen Datens{\"a}tze und der umfangreichen Berechnungen behandelt ein Großteil dieser Arbeit auch Aufwandsbetrachtungen und Fragen der effizienten Berechnung. Kantendetektoren in verschiedenen Phasen und mit haupts{\"a}chlich horizontaler Orientierung stellen die wichtigsten aus der Analyse hervorgehenden Funktionen dar. Eine Anwendung auf konkrete Navigationsaufgaben des Roboters konnte bisher nicht erreicht werden. Eine visuelle Interpretation der erlernten Merkmale ist jedoch durchaus gegeben.

PDF [BibTex]

PDF [BibTex]


no image
Semi-Supervised Learning

Chapelle, O., Schölkopf, B., Zien, A.

pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Abstract
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

Web [BibTex]

Web [BibTex]


no image
An Online-Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems

Deisenroth, MP.

Biologische Kybernetik, Universität Karlsruhe (TH), Karlsruhe, Germany, August 2006 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


no image
Object Classification using Local Image Features

Nowozin, S.

Biologische Kybernetik, Technical University of Berlin, Berlin, Germany, May 2006 (diplomathesis)

Abstract
Object classification in digital images remains one of the most challenging tasks in computer vision. Advances in the last decade have produced methods to repeatably extract and describe characteristic local features in natural images. In order to apply machine learning techniques in computer vision systems, a representation based on these features is needed. A set of local features is the most popular representation and often used in conjunction with Support Vector Machines for classification problems. In this work, we examine current approaches based on set representations and identify their shortcomings. To overcome these shortcomings, we argue for extending the set representation into a graph representation, encoding more relevant information. Attributes associated with the edges of the graph encode the geometric relationships between individual features by making use of the meta data of each feature, such as the position, scale, orientation and shape of the feature region. At the same time all invariances provided by the original feature extraction method are retained. To validate the novel approach, we use a standard subset of the ETH-80 classification benchmark.

PDF [BibTex]

PDF [BibTex]


no image
Kernel PCA for Image Compression

Huhle, B.

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

PDF [BibTex]

PDF [BibTex]


no image
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)

PDF [BibTex]

PDF [BibTex]


no image
Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Web [BibTex]

Web [BibTex]


no image
Semigroups applied to transport and queueing processes

Radl, A.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, 2006 (phdthesis)

PDF [BibTex]

PDF [BibTex]


no image
Local Alignment Kernels for Protein Homology Detection

Saigo, H.

Biologische Kybernetik, Kyoto University, Kyoto, Japan, 2006 (phdthesis)

[BibTex]

[BibTex]


no image
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].

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
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.

PDF Web [BibTex]

PDF Web [BibTex]

1997


no image
Support vector learning

Schölkopf, B.

pages: 173, Oldenbourg, München, Germany, 1997, Zugl.: Berlin, Techn. Univ., Diss., 1997 (book)

PDF GZIP [BibTex]

1997

PDF GZIP [BibTex]