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)

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)

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)

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)

Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering

Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M.

2015 (misc)

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mms Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M. Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering 2015 (misc)

publisher link (url) [BibTex]

2014

Advanced Structured Prediction

Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H.

Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

Abstract

The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

2014

ps Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H. Advanced Structured Prediction Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

publisher link (url) [BibTex]

Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

arXiv preprint, March 2014, clmc (misc)

Abstract

Abstract: Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally. An interesting feature of locally weighted regression is that it can work with ...

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am pn Meier, F., Hennig, P., Schaal, S. Local Gaussian Regression arXiv preprint, March 2014, clmc (misc)

Fibrillar structures to reduce viscous drag on aerodynamic and hydrodynamic wall surfaces

Castillo, L., Aksak, B., Sitti, M.

March 2014, US Patent App. 14/774,767 (misc)

pi Castillo, L., Aksak, B., Sitti, M. Fibrillar structures to reduce viscous drag on aerodynamic and hydrodynamic wall surfaces March 2014, US Patent App. 14/774,767 (misc)

The design of microfibers with mushroom-shaped tips for optimal adhesion

Sitti, M., Aksak, B.

February 2014, US Patent App. 14/766,561 (misc)

pi Sitti, M., Aksak, B. The design of microfibers with mushroom-shaped tips for optimal adhesion February 2014, US Patent App. 14/766,561 (misc)

Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

ei Kober, J., Peters, J. Learning Motor Skills: From Algorithms to Robot Experiments 97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

The Randomized Dependence Coefficient (UC Berkeley, MSKCC, MIT, Harvard, 2013)

Lopez-Paz, D.

2014 (talk)

ei Lopez-Paz, D. The Randomized Dependence Coefficient (UC Berkeley, MSKCC, MIT, Harvard, 2013) 2014 (talk)

Unsupervised identification of neural events in local field potentials

Besserve, M., Schölkopf, B., Logothetis, N. K.

44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)

ei Besserve, M., Schölkopf, B., Logothetis, N. K. Unsupervised identification of neural events in local field potentials 44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)

Quantifying statistical dependency

Besserve, M.

Research Network on Learning Systems Summer School, 2014 (talk)

ei Besserve, M. Quantifying statistical dependency Research Network on Learning Systems Summer School, 2014 (talk)

slides_Monday slides_Tuesday slides_Wednesday slides_Thursday slides_Friday [BibTex]

Causal Inference from Passive Observations

Janzing, D.

24th Summer School University of Jyväskylā, Finland, August, 2014 (talk)

ei Janzing, D. Causal Inference from Passive Observations 24th Summer School University of Jyväskylā, Finland, August, 2014 (talk)

slides_Monday slides_Tuesday slides_Wednesday slides_Thursday slides_Friday [BibTex]

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)

Kernel Methods in Computational Biology

Schölkopf, B., Tsuda, K., Vert, J.

pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

Abstract

Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology. Following three introductory chaptersan introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biologythe book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.

ei Schölkopf, B., Tsuda, K., Vert, J. Kernel Methods in Computational Biology pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

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)

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)

Statistische Lerntheorie und Empirische Inferenz

Schölkopf, B.

Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

Abstract

Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.

PDF Web [BibTex]

ei Schölkopf, B. Statistische Lerntheorie und Empirische Inferenz Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

PDF Web [BibTex]

Advanced Statistical Learning Theory

Machine Learning Summer School, 2004 (talk)

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

Nanoscale Materials for Energy Storage

{Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

mms

mms Nanoscale Materials for Energy Storage {Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

2003

Statistical Learning Theory

Machine Learning Summer School, August 2003 (talk)

2003

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)

Magnetism and the Microstructure of Ferromagnetic Solids

Kronmüller, H., Fähnle, M.

pages: 432 p., 1st ed., Cambridge University Press, Cambridge, 2003 (book)

mms

mms Kronmüller, H., Fähnle, M. Magnetism and the Microstructure of Ferromagnetic Solids pages: 432 p., 1st ed., Cambridge University Press, Cambridge, 2003 (book)

2000

Advances in Large Margin Classifiers

Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D.

pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)

Abstract

The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

2000

ei Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. Advances in Large Margin Classifiers pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)