Publications | Max Planck Institute for Intelligent Systems

ei Chapelle, O. A taxonomy of semi-supervised learning algorithms Yahoo!, December 2005 (talk)

Building Sparse Large Margin Classifiers

Wu, M., Schölkopf, B., BakIr, G.

The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

ei Wu, M., Schölkopf, B., BakIr, G. Building Sparse Large Margin Classifiers The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

Learning from Labeled and Unlabeled Data on a Directed Graph

The 22nd International Conference on Machine Learning, August 2005 (talk)

Abstract

We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.

ei Zhou, D. Learning from Labeled and Unlabeled Data on a Directed Graph The 22nd International Conference on Machine Learning, August 2005 (talk)

Machine-Learning Approaches to BCI in Tübingen

Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M.

Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

[BibTex]

ei Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M. Machine-Learning Approaches to BCI in Tübingen Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

[BibTex]

Kernel Constrained Covariance for Dependence Measurement

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N.

AISTATS, January 2005 (talk)

Abstract

We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables in all circumstances. Dependent random variables can result in a COCO which is arbitrarily close to zero when the source densities are highly non-smooth. All current kernel-based independence tests share this behaviour. We demonstrate exponential convergence between the population and empirical COCO. Finally, we use COCO as a measure of joint neural activity between voxels in MRI recordings of the macaque monkey, and compare the results to the mutual information and the correlation. We also show the effect of removing breathing artefacts from the MRI recording.

PostScript [BibTex]

ei Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N. Kernel Constrained Covariance for Dependence Measurement AISTATS, January 2005 (talk)

PostScript [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)

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)

Advanced Statistical Learning Theory

Machine Learning Summer School, 2004 (talk)

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

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)