Publications | Max Planck Institute for Intelligent Systems

Independence of cause and mechanism in brain networks

Besserve, M.

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

ei Besserve, M. Independence of cause and mechanism in brain networks DALI workshop on Networks: Processes and Causality, April 2015 (talk)

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]

ei Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D. Information-Theoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

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]

ei Foreman-Mackey, D., Hogg, D. W., Schölkopf, B. The search for single exoplanet transits in the Kepler light curves IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

2013

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)

2013

ei 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)

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)

ei 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)

Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

ei Deisenroth, M., Szepesvári, C., Peters, J. Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24 pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

Domain Generalization via Invariant Feature Representation

Muandet, K.

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

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

Lopez-Paz, D., Duvenaud, D.

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

2013 (talk)

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

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.

2009

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

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.

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

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.

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

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.

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

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.

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

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.

ei Hill, NJ., Mellinger, J. Implementing a Signal Processing Filter in BCI2000 Using C++ Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

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.

ei Kober, J., Peters, J., Oztop, E. Learning Motor Primitives for Robotics Advanced Telecommunications Research Center ATR, June 2009 (talk)

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)

ei Lampert, C. Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

2004

How to learn from very few examples?

October 2004 (talk)

2004

ei Zhou, D. How to learn from very few examples? October 2004 (talk)

Discrete vs. Continuous: Two Sides of Machine Learning

October 2004 (talk)

ei Zhou, D. Discrete vs. Continuous: Two Sides of Machine Learning October 2004 (talk)

Discrete vs. Continuous: Two Sides of Machine Learning

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.

ei Zhou, D. Discrete vs. Continuous: Two Sides of Machine Learning October 2004 (talk)

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)

ei Eichhorn, J. Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung September 2004 (talk)

Bousquet, O., von Luxburg, U., Rätsch, G.

Advanced Lectures on Machine Learning

ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Abstract

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in T{\"u}bingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

ei Bousquet, O., von Luxburg, U., Rätsch, G. Advanced Lectures on Machine Learning ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175

Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B.

Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

Web DOI [BibTex]

ei Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B. Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175 Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

Web DOI [BibTex]

Riemannian Geometry on Graphs and its Application to Ranking and Classification

June 2004 (talk)

Abstract

ei Zhou, D. Riemannian Geometry on Graphs and its Application to Ranking and Classification June 2004 (talk)

Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference

Thrun, S., Saul, L., Schölkopf, B.

Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

Abstract

The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendeesphysicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

ei Thrun, S., Saul, L., Schölkopf, B. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

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

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.

ei Zhou, D. Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking January 2004 (talk)

Introduction to Category Theory

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.

ei Bousquet, O. Introduction to Category Theory Internal Seminar, January 2004 (talk)

Advanced Statistical Learning Theory

Machine Learning Summer School, 2004 (talk)

ei Bousquet, O. Advanced Statistical Learning Theory Machine Learning Summer School, 2004 (talk)

2003

Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), LNCS Vol. 2777

Schölkopf, B., Warmuth, M.

Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), COLT/Kernel 2003, pages: 746, Springer, Berlin, Germany, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, November 2003, Lecture Notes in Computer Science ; 2777 (proceedings)

DOI [BibTex]

2003

ei Schölkopf, B., Warmuth, M. Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), LNCS Vol. 2777 Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), COLT/Kernel 2003, pages: 746, Springer, Berlin, Germany, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, November 2003, Lecture Notes in Computer Science ; 2777 (proceedings)

DOI [BibTex]

Statistical Learning Theory

Machine Learning Summer School, August 2003 (talk)

ei Bousquet, O. Statistical Learning Theory Machine Learning Summer School, August 2003 (talk)

Remarks on Statistical Learning Theory

Machine Learning Summer School, August 2003 (talk)

ei Bousquet, O. Remarks on Statistical Learning Theory Machine Learning Summer School, August 2003 (talk)

Rademacher and Gaussian averages in Learning Theory

Universite de Marne-la-Vallee, March 2003 (talk)

ei Bousquet, O. Rademacher and Gaussian averages in Learning Theory Universite de Marne-la-Vallee, March 2003 (talk)

Bousquet, O., Schölkopf, B.

Statistical Learning Theory

March 2003 (talk)

ei Bousquet, O., Schölkopf, B. Statistical Learning Theory March 2003 (talk)

Concentration Inequalities and Data-Dependent Error Bounds

Uni. Jena, February 2003 (talk)

ei Bousquet, O. Concentration Inequalities and Data-Dependent Error Bounds Uni. Jena, February 2003 (talk)

Introduction: Robots with Cognition?

Franz, MO.

6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

Abstract

Using robots as models of cognitive behaviour has a long tradition in robotics. Parallel to the historical development in cognitive science, one observes two major, subsequent waves in cognitive robotics. The first is based on ideas of classical, cognitivist Artificial Intelligence (AI). According to the AI view of cognition as rule-based symbol manipulation, these robots typically try to extract symbolic descriptions of the environment from their sensors that are used to update a common, global world representation from which, in turn, the next action of the robot is derived. The AI approach has been successful in strongly restricted and controlled environments requiring well-defined tasks, e.g. in industrial assembly lines. AI-based robots mostly failed, however, in the unpredictable and unstructured environments that have to be faced by mobile robots. This has provoked the second wave in cognitive robotics which tries to achieve cognitive behaviour as an emergent property from the interaction of simple, low-level modules. Robots of the second wave are called animats as their architecture is designed to closely model aspects of real animals. Using only simple reactive mechanisms and Hebbian-type or evolutionary learning, the resulting animats often outperformed the highly complex AI-based robots in tasks such as obstacle avoidance, corridor following etc. While successful in generating robust, insect-like behaviour, typical animats are limited to stereotyped, fixed stimulus-response associations. If one adopts the view that cognition requires a flexible, goal-dependent choice of behaviours and planning capabilities (H.A. Mallot, Kognitionswissenschaft, 1999, 40-48) then it appears that cognitive behaviour cannot emerge from a collection of purely reactive modules. It rather requires environmentally decoupled structures that work without directly engaging the actions that it is concerned with. This poses the current challenge to cognitive robotics: How can we build cognitive robots that show the robustness and the learning capabilities of animats without falling back into the representational paradigm of AI? The speakers of the symposium present their approaches to this question in the context of robot navigation and sensorimotor learning. In the first talk, Prof. Helge Ritter introduces a robot system for imitation learning capable of exploring various alternatives in simulation before actually performing a task. The second speaker, Angelo Arleo, develops a model of spatial memory in rat navigation based on his electrophysiological experiments. He validates the model on a mobile robot which, in some navigation tasks, shows a performance comparable to that of the real rat. A similar model of spatial memory is used to investigate the mechanisms of territory formation in a series of robot experiments presented by Prof. Hanspeter Mallot. In the last talk, we return to the domain of sensorimotor learning where Ralf M{\"o}ller introduces his approach to generate anticipatory behaviour by learning forward models of sensorimotor relationships.

ei Franz, MO. Introduction: Robots with Cognition? 6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)