Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

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Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A. Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

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Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

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Burger, HC. Modelling and Learning Approaches to Image Denoising Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

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MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures

Mantlik, F., Bezrukov, I., Hofmann, M., Schölkopf, B., Pichler, B.

IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)

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Mantlik, F., Bezrukov, I., Hofmann, M., Schölkopf, B., Pichler, B. MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)

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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

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Muandet, K. Domain Generalization via Invariant Feature Representation 30th International Conference on Machine Learning (ICML2013), 2013 (talk)

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Random Projections (Computational and Biological Learning Lab, University of Cambridge, 2013)

Lopez-Paz, D., Duvenaud, D.

2013 (talk)

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Lopez-Paz, D., Duvenaud, D. Random Projections (Computational and Biological Learning Lab, University of Cambridge, 2013) 2013 (talk)

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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

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Lippert, C. Linear mixed models for genome-wide association studies University of Tübingen, Germany, 2013 (phdthesis)

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

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

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Mülling, K. Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis Technical University Darmstadt, Germany, 2013 (phdthesis)

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Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Wang, Z.

Technical University Darmstadt, Germany, 2013 (phdthesis)

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Wang, Z. Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models Technical University Darmstadt, Germany, 2013 (phdthesis)

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2012

Support Vector Machines, Support Measure Machines, and Quasar Target Selection

Muandet, K.

Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

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2012

Muandet, K. Support Vector Machines, Support Measure Machines, and Quasar Target Selection Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

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Hilbert Space Embedding for Dirichlet Process Mixtures

Muandet, K.

NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

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Muandet, K. Hilbert Space Embedding for Dirichlet Process Mixtures NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

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Scalable graph kernels

Shervashidze, N.

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

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

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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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Simultaneous small animal PET/MR in activated and resting state reveals multiple brain networks

Wehrl, H., Lankes, K., Hossain, M., Bezrukov, I., Liu, C., Martirosian, P., Schick, F., Pichler, B.

20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

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Wehrl, H., Lankes, K., Hossain, M., Bezrukov, I., Liu, C., Martirosian, P., Schick, F., Pichler, B. Simultaneous small animal PET/MR in activated and resting state reveals multiple brain networks 20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

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A new PET insert for simultaneous PET/MR small animal imaging

Wehrl, H., Lankes, K., Hossain, M., Bezrukov, I., Liu, C., Martirosian, P., Reischl, G., Schick, F., Pichler, B.

20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

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Wehrl, H., Lankes, K., Hossain, M., Bezrukov, I., Liu, C., Martirosian, P., Reischl, G., Schick, F., Pichler, B. A new PET insert for simultaneous PET/MR small animal imaging 20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

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Evaluation of a new, large field of view, small animal PET/MR system

Hossain, M., Wehrl, H., Lankes, K., Liu, C., Bezrukov, I., Reischl, G., Pichler, B.

50. Jahrestagung der Deutschen Gesellschaft fuer Nuklearmedizin (NuklearMedizin), April 2012 (talk)

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Hossain, M., Wehrl, H., Lankes, K., Liu, C., Bezrukov, I., Reischl, G., Pichler, B. Evaluation of a new, large field of view, small animal PET/MR system 50. Jahrestagung der Deutschen Gesellschaft fuer Nuklearmedizin (NuklearMedizin), April 2012 (talk)

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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

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

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Simultaneous small animal PET/MR reveals different brain networks during stimulation and rest

Wehrl, H., Hossain, M., Lankes, K., Liu, C., Bezrukov, I., Martirosian, P., Reischl, G., Schick, F., Pichler, B.

World Molecular Imaging Congress (WMIC), 2012 (talk)

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Wehrl, H., Hossain, M., Lankes, K., Liu, C., Bezrukov, I., Martirosian, P., Reischl, G., Schick, F., Pichler, B. Simultaneous small animal PET/MR reveals different brain networks during stimulation and rest World Molecular Imaging Congress (WMIC), 2012 (talk)

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Support Measure Machines for Quasar Target Selection

Muandet, K.

Astro Imaging Workshop, 2012 (talk)

Abstract

In this talk I will discuss the problem of quasar target selection. The objects attributes in astronomy such as fluxes are often subjected to substantial and heterogeneous measurement uncertainties, especially for the medium-redshift between 2.2 and 3.5 quasars which is relatively rare and must be targeted down to g ~ 22 mag. Most of the previous works for quasar target selection includes UV-excess, kernel density estimation, a likelihood approach, and artificial neural network cannot directly deal with the heterogeneous input uncertainties. Recently, extreme deconvolution (XD) has been used to tackle this problem in a well-posed manner. In this work, we present a discriminative approach for quasar target selection that can deal with input uncertainties directly. To do so, we represent each object as a Gaussian distribution whose mean is the object's attribute vector and covariance is the given flux measurement uncertainty. Given a training set of Gaussian distributions, the support measure machines (SMMs) algorithm are trained and used to build the quasar targeting catalog. Preliminary results will also be presented. Joint work with Jo Bovy and Bernhard Sch{\"o}lkopf

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Muandet, K. Support Measure Machines for Quasar Target Selection Astro Imaging Workshop, 2012 (talk)

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PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics

Seldin, Y.

Workshop on Statistical Physics of Inference and Control Theory, 2012 (talk)

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Seldin, Y. PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics Workshop on Statistical Physics of Inference and Control Theory, 2012 (talk)

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PET Performance Measurements of a Next Generation Dedicated Small Animal PET/MR Scanner

Liu, C., Hossain, M., Lankes, K., Bezrukov, I., Wehrl, H., Kolb, A., Judenhofer, M., Pichler, B.

Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), 2012 (talk)

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Liu, C., Hossain, M., Lankes, K., Bezrukov, I., Wehrl, H., Kolb, A., Judenhofer, M., Pichler, B. PET Performance Measurements of a Next Generation Dedicated Small Animal PET/MR Scanner Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), 2012 (talk)

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Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

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

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

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Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

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

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PAC-Bayesian Analysis of Supervised, Unsupervised, and Reinforcement Learning

Seldin, Y., Laviolette, F., Shawe-Taylor, J.

Tutorial at the 29th International Conference on Machine Learning (ICML), 2012 (talk)

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Seldin, Y., Laviolette, F., Shawe-Taylor, J. PAC-Bayesian Analysis of Supervised, Unsupervised, and Reinforcement Learning Tutorial at the 29th International Conference on Machine Learning (ICML), 2012 (talk)

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Influence of MR-based attenuation correction on lesions within bone and susceptibility artifact regions

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Pichler, B.

Molekulare Bildgebung (MoBi), 2012 (talk)

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Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Pichler, B. Influence of MR-based attenuation correction on lesions within bone and susceptibility artifact regions Molekulare Bildgebung (MoBi), 2012 (talk)

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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)

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Structured Apprenticeship Learning

Boularias, A., Kroemer, O., Peters, J.

European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

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Boularias, A., Kroemer, O., Peters, J. Structured Apprenticeship Learning European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

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PAC-Bayesian Analysis and Its Applications

Seldin, Y., Laviolette, F., Shawe-Taylor, J.

Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (talk)

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Seldin, Y., Laviolette, F., Shawe-Taylor, J. PAC-Bayesian Analysis and Its Applications Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (talk)

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Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise

Deisenroth, M., Peters, J.

European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

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Deisenroth, M., Peters, J. Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

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Kernel Bellman Equations in POMDPs

Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K.

Technical Committee on Infomation-Based Induction Sciences and Machine Learning (IBISML'12), 2012 (talk)

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Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K. Kernel Bellman Equations in POMDPs Technical Committee on Infomation-Based Induction Sciences and Machine Learning (IBISML'12), 2012 (talk)

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Beta oscillations propagate as traveling waves in the macaque prefrontal cortex

Panagiotaropoulos, T., Besserve, M., Logothetis, N.

42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (talk)

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Panagiotaropoulos, T., Besserve, M., Logothetis, N. Beta oscillations propagate as traveling waves in the macaque prefrontal cortex 42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (talk)

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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)

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

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2009

Machine Learning for Brain-Computer Interfaces

Hill, NJ.

Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

Abstract

Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user‘s intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn‘t matter what classifier you use once you‘ve done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I‘ll highlight some of the interesting problems that remain open for machine-learners.

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2009

Hill, NJ. Machine Learning for Brain-Computer Interfaces Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

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PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y.

NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Abstract

Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of "how many clusters are present in the data?", and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering‘s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

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Seldin, Y. PAC-Bayesian Approach to Formulation of Clustering Objectives NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

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Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data

Shelton, JA.

Women in Machine Learning Workshop (WiML), December 2009 (talk)

Abstract

Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This manifold learning approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned and such data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, Laplacian regularization improved performance whereas the semi-supervised variants of KCCA yielded the best performance. We additionally analyze the weights learned by the regression in order to infer brain regions that are important during different types of visual processing.

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Shelton, JA. Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data Women in Machine Learning Workshop (WiML), December 2009 (talk)

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Event-Related Potentials in Brain-Computer Interfacing

Hill, NJ.

Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

Abstract

An introduction to event-related potentials with specific reference to their use in brain-computer interfacing applications and research.

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Hill, NJ. Event-Related Potentials in Brain-Computer Interfacing Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

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BCI2000 and Python

Hill, NJ.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract

A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 software package.

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Hill, NJ. BCI2000 and Python Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

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