Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Kober, J. Reinforcement Learning for Motor Primitives Biologische Kybernetik, University of Stuttgart, Stuttgart, Germany, August 2008 (diplomathesis)

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Asymmetries of Time Series under Inverting their Direction

Peters, J.

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

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Peters, J. Asymmetries of Time Series under Inverting their Direction Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)

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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)

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Kienzle, W. Learning an Interest Operator from Human Eye Movements Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2008 (phdthesis)

[BibTex]

Causal inference from statistical data

Sun, X.

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

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Sun, X. Causal inference from statistical data Biologische Kybernetik, Technische Hochschule Karlsruhe, Karlsruhe, Germany, April 2008 (phdthesis)

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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]

Berens, P. Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex Biologische Kybernetik, Eberhard Karls Universität Tübingen, Tübingen, Germany, April 2008 (diplomathesis)

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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)

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Schreiner, T. Development and Application of a Python Scripting Framework for BCI2000 Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, January 2008 (diplomathesis)

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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.

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Walder, CJ. Efficient and Invariant Regularisation with Application to Computer Graphics Biologische Kybernetik, University of Queensland, Brisbane, Australia, January 2008 (phdthesis)

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2006

Kernel PCA for Image Compression

Huhle, B.

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

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2006

Huhle, B. Kernel PCA for Image Compression Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

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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)

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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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2019

Learning Transferable Representations

2019

Sample-efficient deep reinforcement learning for continuous control

Formally justified and modular Bayesian inference for probabilistic programs

Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Pragmatism and Variable Transformations in Causal Modelling

Quantification of tumor heterogeneity using PET/MRI and machine learning

Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning

2017

Nonparametric Disturbance Correction and Nonlinear Dual Control

2017

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

2008

Reinforcement Learning for Motor Primitives

2008

Asymmetries of Time Series under Inverting their Direction

Learning an Interest Operator from Human Eye Movements

Causal inference from statistical data

Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex

Development and Application of a Python Scripting Framework for BCI2000

Efficient and Invariant Regularisation with Application to Computer Graphics

2006

Kernel PCA for Image Compression

2006

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

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