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2010


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Approximate Inference in Graphical Models

Hennig, P.

University of Cambridge, November 2010 (phdthesis)

Web [BibTex]

2010

Web [BibTex]


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Bayesian Inference and Experimental Design for Large Generalised Linear Models

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2010 (phdthesis)

PDF Web [BibTex]

PDF Web [BibTex]


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Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Predictive Representations For Sequential Decision Making Under Uncertainty

Boularias, A.

Université Laval, Quebec, Canada, July 2010 (phdthesis)

Abstract
The problem of making decisions is ubiquitous in life. This problem becomes even more complex when the decisions should be made sequentially. In fact, the execution of an action at a given time leads to a change in the environment of the problem, and this change cannot be predicted with certainty. The aim of a decision-making process is to optimally select actions in an uncertain environment. To this end, the environment is often modeled as a dynamical system with multiple states, and the actions are executed so that the system evolves toward a desirable state. In this thesis, we proposed a family of stochastic models and algorithms in order to improve the quality of of the decision-making process. The proposed models are alternative to Markov Decision Processes, a largely used framework for this type of problems. In particular, we showed that the state of a dynamical system can be represented more compactly if it is described in terms of predictions of certain future events. We also showed that even the cognitive process of selecting actions, known as policy, can be seen as a dynamical system. Starting from this observation, we proposed a panoply of algorithms, all based on predictive policy representations, in order to solve different problems of decision-making, such as decentralized planning, reinforcement learning, or imitation learning. We also analytically and empirically demonstrated that the proposed approaches lead to a decrease in the computational complexity and an increase in the quality of the decisions, compared to standard approaches for planning and learning under uncertainty.

PDF [BibTex]


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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET)

Mantlik, F.

Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

[BibTex]

[BibTex]


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Finding Gene-Gene Interactions using Support Vector Machines

Rakitsch, B.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

[BibTex]

[BibTex]


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Structural and Relational Data Mining for Systems Biology Applications

Georgii, E.

Eberhard Karls Universität Tübingen, Germany , 2010 (phdthesis)

Web [BibTex]

Web [BibTex]


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Population Coding in the Visual System: Statistical Methods and Theory

Macke, J.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

[BibTex]

[BibTex]


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Bayesian Methods for Neural Data Analysis

Gerwinn, S.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

Web [BibTex]

Web [BibTex]


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Clustering with Neighborhood Graphs

Maier, M.

Universität des Saarlandes, Saarbrücken, Germany, 2010 (phdthesis)

Web [BibTex]

Web [BibTex]


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Detecting and modeling time shifts in microarray time series data applying Gaussian processes

Zwießele, M.

Eberhard Karls Universität Tübingen, Germany, 2010 (thesis)

[BibTex]

[BibTex]


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Detecting the mincut in sparse random graphs

Köhler, R.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

[BibTex]

[BibTex]


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A wider view on encoding and decoding in the visual brain-computer interface speller system

Martens, S.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

[BibTex]

2008


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Reinforcement Learning for Motor Primitives

Kober, J.

Biologische Kybernetik, University of Stuttgart, Stuttgart, Germany, August 2008 (diplomathesis)

PDF [BibTex]

2008

PDF [BibTex]


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Asymmetries of Time Series under Inverting their Direction

Peters, J.

Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Learning an Interest Operator from Human Eye Movements

Kienzle, W.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2008 (phdthesis)

[BibTex]

[BibTex]


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Causal inference from statistical data

Sun, X.

Biologische Kybernetik, Technische Hochschule Karlsruhe, Karlsruhe, Germany, April 2008 (phdthesis)

Web [BibTex]

Web [BibTex]


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Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex

Berens, P.

Biologische Kybernetik, Eberhard Karls Universität Tübingen, Tübingen, Germany, April 2008 (diplomathesis)

[BibTex]

[BibTex]


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Development and Application of a Python Scripting Framework for BCI2000

Schreiner, T.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, January 2008 (diplomathesis)

[BibTex]

[BibTex]


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Efficient and Invariant Regularisation with Application to Computer Graphics

Walder, CJ.

Biologische Kybernetik, University of Queensland, Brisbane, Australia, January 2008 (phdthesis)

Abstract
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse problems which arise in, for example, machine learning and computer graphics, have been treated with practical success using methods based on a reproducing kernel Hilbert space perspective. This perspective is often theoretically convenient, in that many functional analysis problems reduce to linear algebra problems in these spaces. Somewhat more complex is the case of conditionally positive definite kernels, and we provide an introduction to both cases, deriving in a particularly elementary manner some key results for the conditionally positive definite case. A common complaint of the practitioner is the long running time of these kernel based algorithms. We provide novel ways of alleviating these problems by essentially using a non-standard function basis which yields computational advantages. That said, by doing so we must also forego the aforementioned theoretical conveniences, and hence need some additional analysis which we provide in order to make the approach practicable. We demonstrate that the method leads to state of the art performance on the problem of surface reconstruction from points. We also provide some analysis of kernels invariant to transformations such as translation and dilation, and show that this indicates the value of learning algorithms which use conditionally positive definite kernels. Correspondingly, we provide a few approaches for making such algorithms practicable. We do this either by modifying the kernel, or directly solving problems with conditionally positive definite kernels, which had previously only been solved with positive definite kernels. We demonstrate the advantage of this approach, in particular by attaining state of the art classification performance with only one free parameter.

PDF [BibTex]

PDF [BibTex]

2005


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Extension to Kernel Dependency Estimation with Applications to Robotics

BakIr, G.

Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

Abstract
Kernel Dependency Estimation(KDE) is a novel technique which was designed to learn mappings between sets without making assumptions on the type of the involved input and output data. It learns the mapping in two stages. In a first step, it tries to estimate coordinates of a feature space representation of elements of the set by solving a high dimensional multivariate regression problem in feature space. Following this, it tries to reconstruct the original representation given the estimated coordinates. This thesis introduces various algorithmic extensions to both stages in KDE. One of the contributions of this thesis is to propose a novel linear regression algorithm that explores low-dimensional subspaces during learning. Furthermore various existing strategies for reconstructing patterns from feature maps involved in KDE are discussed and novel pre-image techniques are introduced. In particular, pre-image techniques for data-types that are of discrete nature such as graphs and strings are investigated. KDE is then explored in the context of robot pose imitation where the input is a an image with a human operator and the output is the robot articulated variables. Thus, using KDE, robot pose imitation is formulated as a regression problem.

PDF PDF [BibTex]

2005

PDF PDF [BibTex]


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Geometrical aspects of statistical learning theory

Hein, M.

Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Implicit Surfaces For Modelling Human Heads

Steinke, F.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

[BibTex]

[BibTex]


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Machine Learning Methods for Brain-Computer Interdaces

Lal, TN.

Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

Web [BibTex]

Web [BibTex]


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Liver Perfusion using Level Set Methods

Nowozin, S.

Biologische Kybernetik, Shanghai JiaoTong University, Shanghai, China, July 2005 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Discriminative Methods for Label Sequence Learning

Altun, Y.

Brown University, Providence, RI, USA, May 2005 (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, TG.

Biologische Kybernetik, Eberhard-Karls University Tübingen, Tübingen, Germany, May 2005 (diplomathesis)

Abstract
A common task in psychophysics is to measure the psychometric function. A psychometric function can be described by its shape and four parameters: offset or threshold, slope or width, false alarm rate or chance level and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. A new Bayesian adaptive psychometric method placing trials by minimising the expected entropy of the posterior probabilty dis- tribution over a set of possible stimuli is introduced. The method is more flexible, faster and at least as efficient as the established method (Kontsevich and Tyler, 1999). Comparably accurate (2dB) threshold and slope estimates can be obtained after about 30 and 500 trials, respectively. By using a dynamic termination criterion the efficiency can be further improved. The method can be applied to all experimental designs including yes/no designs and allows acquisition of any set of free parameters. By weighting the importance of parameters one can include nuisance parameters and adjust the relative expected errors. Use of nuisance parameters may lead to more accurate estimates than assuming a guessed fixed value. Block designs are supported and do not harm the performance if a sufficient number of trials are performed. The method was evaluated by computer simulations in which the role of parametric assumptions, its robustness, the quality of different point estimates, the effect of dynamic termination criteria and many other settings were investigated.

[BibTex]

[BibTex]


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Support Vector Classification of Images with Local Features

Blaschko, MB.

Biologische Kybernetik, University of Massachusetts, Amherst, May 2005 (diplomathesis)

[BibTex]

[BibTex]


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Efficient Pattern Selection for Support Vector Classifiers and its CRM Application

Shin, H.

Biologische Kybernetik, Seoul National University, Seoul, Korea, February 2005 (phdthesis)

PDF [BibTex]

PDF [BibTex]


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Kernels: Regularization and Optimization

Ong, CS.

Biologische Kybernetik, The Australian National University, Canberra, Australia, 2005 (phdthesis)

PDF GZIP [BibTex]

PDF GZIP [BibTex]

2002


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Nonlinear Multivariate Analysis with Geodesic Kernels

Kuss, M.

Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

GZIP [BibTex]

2002

GZIP [BibTex]


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Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms

Bousquet, O.

Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

Abstract
New classification algorithms based on the notion of 'margin' (e.g. Support Vector Machines, Boosting) have recently been developed. The goal of this thesis is to better understand how they work, via a study of their theoretical performance. In order to do this, a general framework for real-valued classification is proposed. In this framework, it appears that the natural tools to use are Concentration Inequalities and Empirical Processes Theory. Thanks to an adaptation of these tools, a new measure of the size of a class of functions is introduced, which can be computed from the data. This allows, on the one hand, to better understand the role of eigenvalues of the kernel matrix in Support Vector Machines, and on the other hand, to obtain empirical model selection criteria.

PostScript [BibTex]


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Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Chapelle, O.

Biologische Kybernetik, 2002 (phdthesis)

Abstract
This thesis presents a theoretical and practical study of Support Vector Machines (SVM) and related learning algorithms. In a first part, we introduce a new induction principle from which SVMs can be derived, but some new algorithms are also presented in this framework. In a second part, after studying how to estimate the generalization error of an SVM, we suggest to choose the kernel parameters of an SVM by minimizing this estimate. Several applications such as feature selection are presented. Finally the third part deals with the incoporation of prior knowledge in a learning algorithm and more specifically, we studied the case of known invariant transormations and the use of unlabeled data.

GZIP [BibTex]

2001


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Variationsverfahren zur Untersuchung von Grundzustandseigenschaften des Ein-Band Hubbard-Modells

Eichhorn, J.

Biologische Kybernetik, Technische Universität Dresden, Dresden/Germany, May 2001 (diplomathesis)

Abstract
Using different modifications of a new variational approach, statical groundstate properties of the one-band Hubbard model such as energy and staggered magnetisation are calculated. By taking into account additional fluctuations, the method ist gradually improved so that a very good description of the energy in one and two dimensions can be achieved. After a detailed discussion of the application in one dimension, extensions for two dimensions are introduced. By use of a modified version of the variational ansatz in particular a description of the quantum phase transition for the magnetisation should be possible.

PostScript [BibTex]

2001

PostScript [BibTex]


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Cerebellar Control of Robot Arms

Peters, J.

Biologische Kybernetik, Technische Univeristät München, München, Germany, 2001 (diplomathesis)

[BibTex]

[BibTex]


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On Unsupervised Learning of Mixtures of Markov Sources

Seldin, Y.

Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, 2001 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Support Vector Machines: Theorie und Anwendung auf Prädiktion epileptischer Anfälle auf der Basis von EEG-Daten

Lal, TN.

Biologische Kybernetik, Institut für Angewandte Mathematik, Universität Bonn, 2001, Advised by Prof. Dr. S. Albeverio (diplomathesis)

ZIP [BibTex]

ZIP [BibTex]

1996


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Evaluation of Gaussian Processes and other Methods for Non-Linear Regression

Rasmussen, CE.

Biologische Kybernetik, Graduate Department of Computer Science, Univeristy of Toronto, 1996 (phdthesis)

PostScript [BibTex]

1996

PostScript [BibTex]