Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Kuss, M. Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

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2005

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.

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2005

BakIr, G. Extension to Kernel Dependency Estimation with Applications to Robotics Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

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Geometrical aspects of statistical learning theory

Hein, M.

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

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Hein, M. Geometrical aspects of statistical learning theory Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

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Implicit Surfaces For Modelling Human Heads

Steinke, F.

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

[BibTex]

Steinke, F. Implicit Surfaces For Modelling Human Heads Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

[BibTex]

Machine Learning Methods for Brain-Computer Interdaces

Lal, TN.

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

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Lal, TN. Machine Learning Methods for Brain-Computer Interdaces Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

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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]

Tanner, TG. Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain Biologische Kybernetik, Eberhard-Karls University Tübingen, Tübingen, Germany, May 2005 (diplomathesis)

[BibTex]

2003

Real-Time Face Detection

Kienzle, W.

Biologische Kybernetik, Eberhard-Karls-Universitaet Tuebingen, Tuebingen, Germany, October 2003 (diplomathesis)

[BibTex]

2003

Kienzle, W. Real-Time Face Detection Biologische Kybernetik, Eberhard-Karls-Universitaet Tuebingen, Tuebingen, Germany, October 2003 (diplomathesis)

[BibTex]

m-Alternative Forced Choice—Improving the Efficiency of the Method of Constant Stimuli

Jäkel, F.

Biologische Kybernetik, Graduate School for Neural and Behavioural Sciences, Tübingen, 2003 (diplomathesis)

[BibTex]

Jäkel, F. m-Alternative Forced Choice—Improving the Efficiency of the Method of Constant Stimuli Biologische Kybernetik, Graduate School for Neural and Behavioural Sciences, Tübingen, 2003 (diplomathesis)

[BibTex]

2017

The right tool for the right question — beyond the encoding versus decoding dichotomy

2017

Nonparametric Disturbance Correction and Nonlinear Dual Control

Probabilistic Active Learning of Functions in Structural Causal Models

Development and Evaluation of a Portable BCI System for Remote Data Acquisition

Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis

Causal models for decision making via integrative inference

Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation

2013

Camera-specific Image Denoising

2013

Modelling and Learning Approaches to Image Denoising

Linear mixed models for genome-wide association studies

Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

2006

Kernel PCA for Image Compression

2006

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

2005

Extension to Kernel Dependency Estimation with Applications to Robotics

2005

Geometrical aspects of statistical learning theory

Implicit Surfaces For Modelling Human Heads

Machine Learning Methods for Brain-Computer Interdaces

Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

2003

Real-Time Face Detection

2003

m-Alternative Forced Choice—Improving the Efficiency of the Method of Constant Stimuli

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