Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

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)

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Schökopf, B. Maschinelles Lernen: Entwicklung ohne Grenzen? 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)

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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

link (url) Project Page [BibTex]

Mahsereci, M. Probabilistic Approaches to Stochastic Optimization Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

link (url) Project Page [BibTex]

Improving Tissue Differentiation based on Optical Emission Spectroscopy for Guided Electrosurgical Tumor Resection with Machine Learning

Zabel, S.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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Zabel, S. Improving Tissue Differentiation based on Optical Emission Spectroscopy for Guided Electrosurgical Tumor Resection with Machine Learning Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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Reinforcement Learning for High-Speed Robotics with Muscular Actuation

Guist, S.

Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

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Guist, S. Reinforcement Learning for High-Speed Robotics with Muscular Actuation Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

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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)

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Wichmann, F. A., Jäkel, F. Methods in Psychophysics In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

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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]

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M. Transfer Learning for BCIs 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]

Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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Schober, M. Probabilistic Ordinary Differential Equation Solvers — Theory and Applications Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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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)

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Jayaram, V. A machine learning approach to taking EEG-based computer interfaces out of the lab Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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2015

easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies

Grimm, Dominik

Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

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2015

Grimm, Dominik easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

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Causal Discovery Beyond Conditional Independences

Sgouritsa, E.

Eberhard Karls Universität Tübingen, Germany, October 2015 (phdthesis)

link (url) [BibTex]

Sgouritsa, E. Causal Discovery Beyond Conditional Independences Eberhard Karls Universität Tübingen, Germany, October 2015 (phdthesis)

link (url) [BibTex]

From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding

Muandet, K.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Muandet, K. From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Machine Learning Approaches to Image Deconvolution

Schuler, C.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Schuler, C. Machine Learning Approaches to Image Deconvolution University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Kernel methods in medical imaging

Charpiat, G., Hofmann, M., Schölkopf, B.

In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

Web link (url) [BibTex]

Charpiat, G., Hofmann, M., Schölkopf, B. Kernel methods in medical imaging In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

Web link (url) [BibTex]

Blind Retrospective Motion Correction of MR Images

Loktyushin, A.

University of Tübingen, Germany, May 2015 (phdthesis)

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Loktyushin, A. Blind Retrospective Motion Correction of MR Images University of Tübingen, Germany, May 2015 (phdthesis)

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Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L. J., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

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O’Donnell, L. J., Schultz, T. Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Hohmann, M. A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

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Gao, M. Sequential Image Deconvolution Using Probabilistic Linear Algebra Technical University of Munich, Germany, 2015 (mastersthesis)

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Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Casarsa de Azevedo, L. Causal Inference in Neuroimaging Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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The effect of frowning on attention

Ibarra Chaoul, A.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Ibarra Chaoul, A. The effect of frowning on attention Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B. Justifying Information-Geometric Causal Inference In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

2012

Scalable graph kernels

Shervashidze, N.

Eberhard Karls Universität Tübingen, Germany, October 2012 (phdthesis)

Web [BibTex]

2012

Shervashidze, N. Scalable graph kernels Eberhard Karls Universität Tübingen, Germany, October 2012 (phdthesis)

Web [BibTex]

Probabilistic Modelling of Expression Variation in Modern eQTL Studies

Zwießele, M.

Eberhard Karls Universität Tübingen, Germany, October 2012 (mastersthesis)

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Zwießele, M. Probabilistic Modelling of Expression Variation in Modern eQTL Studies Eberhard Karls Universität Tübingen, Germany, October 2012 (mastersthesis)

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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

PDF [BibTex]

Kober, J. Learning Motor Skills: From Algorithms to Robot Experiments Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

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Expectation-Maximization methods for solving (PO)MDPs and optimal control problems

Toussaint, M., Storkey, A., Harmeling, S.

In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

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Toussaint, M., Storkey, A., Harmeling, S. Expectation-Maximization methods for solving (PO)MDPs and optimal control problems In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

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Active Learning Methods in Classification of Remote Sensing Images

Bruzzone, L., Persello, C., Demir, B.

In Signal and Image Processing for Remote Sensing, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

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Bruzzone, L., Persello, C., Demir, B. Active Learning Methods in Classification of Remote Sensing Images In Signal and Image Processing for Remote Sensing, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

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Inferential structure determination from NMR data

Habeck, M.

In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

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Habeck, M. Inferential structure determination from NMR data In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

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Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Gomez Rodriguez, M. Structure and Dynamics of Diffusion Networks Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Robot Learning

Sigaud, O., Peters, J.

In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

Web [BibTex]

Sigaud, O., Peters, J. Robot Learning In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

Web [BibTex]

Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Abstract

As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

Web DOI [BibTex]

Kober, J., Peters, J. Reinforcement Learning in Robotics: A Survey In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Web DOI [BibTex]

Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

Web [BibTex]

Hirsch, M. Blind Deconvolution in Scientific Imaging & Computational Photography Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

Web [BibTex]

Restricted structural equation models for causal inference

Peters, J.

ETH Zurich, Switzerland, 2012 (phdthesis)

link (url) DOI [BibTex]

Peters, J. Restricted structural equation models for causal inference ETH Zurich, Switzerland, 2012 (phdthesis)

link (url) DOI [BibTex]

Higher-Order Tensors in Diffusion MRI

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, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

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Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L. Higher-Order Tensors in Diffusion MRI In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

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Combinatorial Problems with Submodular Coupling in Machine Learning and Computer Vision

Jegelka, S.

ETH Zürich, Switzerland, 2012 (phdthesis)

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Jegelka, S. Combinatorial Problems with Submodular Coupling in Machine Learning and Computer Vision ETH Zürich, Switzerland, 2012 (phdthesis)

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Mining correlated loci at a genome-wide scale

Velkov, V.

Eberhard Karls Universität Tübingen, Germany, 2012 (mastersthesis)

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Velkov, V. Mining correlated loci at a genome-wide scale Eberhard Karls Universität Tübingen, Germany, 2012 (mastersthesis)

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Automatische Seitenkettenzuordnung zur NMR Proteinstrukturaufklärung mittels ganzzahliger linearer Programmierung

Hooge, J.

University of Tübingen, Germany, 2012 (diplomathesis)

[BibTex]

Hooge, J. Automatische Seitenkettenzuordnung zur NMR Proteinstrukturaufklärung mittels ganzzahliger linearer Programmierung University of Tübingen, Germany, 2012 (diplomathesis)

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Nonparametric System Identification and Control for Periodic Error Correction in Telescopes

Klenske, E. D.

University of Stuttgart, 2012 (diplomathesis)

PDF [BibTex]

Klenske, E. D. Nonparametric System Identification and Control for Periodic Error Correction in Telescopes University of Stuttgart, 2012 (diplomathesis)

PDF [BibTex]

2009

Toward a Theory of Consciousness

Tononi, G., Balduzzi, D.

In The Cognitive Neurosciences, pages: 1201-1220, (Editors: Gazzaniga, M.S.), MIT Press, Cambridge, MA, USA, October 2009 (inbook)

Web [BibTex]

2009

Tononi, G., Balduzzi, D. Toward a Theory of Consciousness In The Cognitive Neurosciences, pages: 1201-1220, (Editors: Gazzaniga, M.S.), MIT Press, Cambridge, MA, USA, October 2009 (inbook)

Web [BibTex]

Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract

This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

PDF [BibTex]

Gehler, PV. Kernel Learning Approaches for Image Classification Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

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A PAC-Bayesian Approach to Structure Learning

Seldin, Y.

Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, September 2009 (phdthesis)

PDF [BibTex]

Seldin, Y. A PAC-Bayesian Approach to Structure Learning Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, September 2009 (phdthesis)

PDF [BibTex]

Text Clustering with Mixture of von Mises-Fisher Distributions

Sra, S., Banerjee, A., Ghosh, J., Dhillon, I.

In Text mining: classification, clustering, and applications, pages: 121-161, Chapman & Hall/CRC data mining and knowledge discovery series, (Editors: Srivastava, A. N. and Sahami, M.), CRC Press, Boca Raton, FL, USA, June 2009 (inbook)

Web DOI [BibTex]

Sra, S., Banerjee, A., Ghosh, J., Dhillon, I. Text Clustering with Mixture of von Mises-Fisher Distributions In Text mining: classification, clustering, and applications, pages: 121-161, Chapman & Hall/CRC data mining and knowledge discovery series, (Editors: Srivastava, A. N. and Sahami, M.), CRC Press, Boca Raton, FL, USA, June 2009 (inbook)

Web DOI [BibTex]

Data Mining for Biologists

Tsuda, K.

In Biological Data Mining in Protein Interaction Networks, pages: 14-27, (Editors: Li, X. and Ng, S.-K.), Medical Information Science Reference, Hershey, PA, USA, May 2009 (inbook)

Abstract

In this tutorial chapter, we review basics about frequent pattern mining algorithms, including itemset mining, association rule mining and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support the application of such methods to larger datasets. We explain technical aspects of the algorithms, but do not go into details. Current biological applications are summarized and possible future directions are given.

Web [BibTex]

Tsuda, K. Data Mining for Biologists In Biological Data Mining in Protein Interaction Networks, pages: 14-27, (Editors: Li, X. and Ng, S.-K.), Medical Information Science Reference, Hershey, PA, USA, May 2009 (inbook)

Web [BibTex]

Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery

Blaschko, MB.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

PDF PDF [BibTex]

Blaschko, MB. Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

PDF PDF [BibTex]

Large Margin Methods for Part of Speech Tagging

Altun, Y.

In Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods, pages: 141-160, (Editors: Keshet, J. and Bengio, S.), Wiley, Hoboken, NJ, USA, January 2009 (inbook)

Web [BibTex]

Altun, Y. Large Margin Methods for Part of Speech Tagging In Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods, pages: 141-160, (Editors: Keshet, J. and Bengio, S.), Wiley, Hoboken, NJ, USA, January 2009 (inbook)

Web [BibTex]

Motor Control and Learning in Table Tennis

Mülling, K.

Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

[BibTex]

Mülling, K. Motor Control and Learning in Table Tennis Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

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