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2015


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[BibTex]

2015

[BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

[BibTex]

[BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

link (url) [BibTex]

2013


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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

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)

Web [BibTex]

2013

Web [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

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

PDF [BibTex]

PDF [BibTex]

2010


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Comparative Quantitative Evaluation of MR-Based Attenuation Correction Methods in Combined Brain PET/MR

Mantlik, F., Hofmann, M., Bezrukov, I., Kolb, A., Beyer, T., Reimold, M., Pichler, B., Schölkopf, B.

2010(M08-4), 2010 Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), November 2010 (talk)

Abstract
Combined PET/MR provides at the same time molecular and functional imaging as well as excellent soft tissue contrast. It does not allow one to directly measure the attenuation properties of scanned tissues, despite the fact that accurate attenuation maps are necessary for quantitative PET imaging. Several methods have therefore been proposed for MR-based attenuation correction (MR-AC). So far, they have only been evaluated on data acquired from separate MR and PET scanners. We evaluated several MR-AC methods on data from 10 patients acquired on a combined BrainPET/MR scanner. This allowed the consideration of specific PET/MR issues, such as the RF coil that attenuates and scatters 511 keV gammas. We evaluated simple MR thresholding methods as well as atlas and machine learning-based MR-AC. CT-based AC served as gold standard reference. To comprehensively evaluate the MR-AC accuracy, we used RoIs from 2 anatomic brain atlases with different levels of detail. Visual inspection of the PET images indicated that even the basic FLASH threshold MR-AC may be sufficient for several applications. Using a UTE sequence for bone prediction in MR-based thresholding occasionally led to false prediction of bone tissue inside the brain, causing a significant overestimation of PET activity. Although it yielded a lower mean underestimation of activity, it exhibited the highest variance of all methods. The atlas averaging approach had a smaller mean error, but showed high maximum overestimation on the RoIs of the more detailed atlas. The Nave Bayes and Atlas-Patch MR-AC yielded the smallest variance, and the Atlas-Patch also showed the smallest mean error. In conclusion, Atlas-based AC using only MR information on the BrainPET/MR yields a high level of accuracy that is sufficient for clinical quantitative imaging requirements. The Atlas-Patch approach was superior to alternative atlas-based methods, yielding a quantification error below 10% for all RoIs except very small ones.

[BibTex]

2010

[BibTex]


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Statistical image analysis and percolation theory

Davies, P., Langovoy, M., Wittich, O.

73rd Annual Meeting of the Institute of Mathematical Statistics (IMS), August 2010 (talk)

Abstract
We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and can be heavy-tailed. We view the object detection problem as hypothesis testing for discrete statistical inverse problems. We present an algorithm that allows to detect objects of various shapes in noisy images. We prove results on consistency and algorithmic complexity of our procedures.

Web [BibTex]

Web [BibTex]


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Statistical image analysis and percolation theory

Langovoy, M., Wittich, O.

28th European Meeting of Statisticians (EMS), August 2010 (talk)

PDF Web [BibTex]

PDF Web [BibTex]


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Cooperative Cuts: Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

24th European Conference on Operational Research (EURO XXIV), July 2010 (talk)

Abstract
We introduce cooperative cut, a minimum cut problem whose cost is a submodular function on sets of edges: the cost of an edge that is added to a cut set depends on the edges in the set. Applications are e.g. in probabilistic graphical models and image processing. We prove NP hardness and a polynomial lower bound on the approximation factor, and upper bounds via four approximation algorithms based on different techniques. Our additional heuristics have attractive practical properties, e.g., to rely only on standard min-cut. Both our algorithms and heuristics appear to do well in practice.

PDF Web [BibTex]

PDF Web [BibTex]


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Solving Large-Scale Nonnegative Least Squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS), June 2010 (talk)

Abstract
We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Han- son [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by ex- ploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established con- vex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

PDF PDF [BibTex]

PDF PDF [BibTex]


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Matrix Approximation Problems

Sra, S.

EU Regional School: Rheinisch-Westf{\"a}lische Technische Hochschule Aachen, May 2010 (talk)

PDF AVI [BibTex]

PDF AVI [BibTex]


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BCI2000 and Python

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

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

PDF [BibTex]

PDF [BibTex]


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Extending BCI2000 Functionality with Your Own C++ Code

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract
A tutorial, with exercises, on how to use BCI2000 C++ framework to write your own real-time signal-processing modules.

[BibTex]

[BibTex]


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Machine-Learning Methods for Decoding Intentional Brain States

Hill, NJ.

Symposium "Non-Invasive Brain Computer Interfaces: Current Developments and Applications" (BIOMAG), March 2010 (talk)

Abstract
Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the user‘s initial intention. The high-noise high-dimensional nature of brain-signals 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 your features are extracted.” Using examples from our own MEG and EEG experiments, I‘ll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than “just” classification, and can be used to find better feature extractors.

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Analysis in Unsupervised Learning

Seldin, Y.

Foundations and New Trends of PAC Bayesian Learning Workshop, March 2010 (talk)

PDF Web [BibTex]

PDF Web [BibTex]


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Learning Motor Primitives for Robotics

Kober, J., Peters, J.

EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality, January 2010 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

[BibTex]

[BibTex]

2009


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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.

PDF Web Web [BibTex]

2009

PDF Web Web [BibTex]


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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.

PDF Web Web [BibTex]

PDF Web Web [BibTex]


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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.

PDF Web [BibTex]


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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.

PDF [BibTex]

PDF [BibTex]


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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.

PDF [BibTex]

PDF [BibTex]


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Implementing a Signal Processing Filter in BCI2000 Using C++

Hill, NJ., Mellinger, J.

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

Abstract
This tutorial shows how the functionality of the BCI2000 software package can be extended with one‘s own code, using BCI2000‘s C++ API.

PDF [BibTex]

PDF [BibTex]


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Randomized algorithms for statistical image analysis based on percolation theory

Davies, P., Langovoy, M., Wittich, O.

27th European Meeting of Statisticians (EMS), July 2009 (talk)

Abstract
We propose a novel probabilistic method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation and random graph theories (see Grimmett (1999)). We address the problem of detection and estimation of signals in situations where the signal-to-noise ratio is particularly low. We present an algorithm that allows to detect objects of various shapes in noisy images. The algorithm has linear complexity and exponential accuracy. Our algorithm substantially di ers from wavelets-based algorithms (see Arias-Castro et.al. (2005)). Moreover, we present an algorithm that produces a crude estimate of an object based on the noisy picture. This algorithm also has linear complexity and is appropriate for real-time systems. We prove results on consistency and algorithmic complexity of our procedures.

Web PDF [BibTex]

Web PDF [BibTex]


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Learning Motor Primitives for Robotics

Kober, J., Peters, J., Oztop, E.

Advanced Telecommunications Research Center ATR, June 2009 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

[BibTex]

[BibTex]


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Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Lampert, C.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

Web [BibTex]

Web [BibTex]


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Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

PDF [BibTex]


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Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract
Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

PDF [BibTex]


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The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative comparative study

Schweikert, G., Luecken, U., Pfeifer, G., Baumeister, W., Plitzko, J.

The thirteenth European Microscopy Congress, August 2004 (talk)

[BibTex]

[BibTex]


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Riemannian Geometry on Graphs and its Application to Ranking and Classification

Zhou, D.

June 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data.

PDF [BibTex]


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Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

Zhou, D.

January 2004 (talk)

Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

PDF [BibTex]


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Introduction to Category Theory

Bousquet, O.

Internal Seminar, January 2004 (talk)

Abstract
A brief introduction to the general idea behind category theory with some basic definitions and examples. A perspective on higher dimensional categories is given.

PDF [BibTex]

PDF [BibTex]


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Advanced Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, 2004 (talk)

PDF [BibTex]

PDF [BibTex]