Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

MPI Papers

MPI Papers

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1998 (38)
1997 (14)
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36 results (View BibTeX file of all listed publications)

2015

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

Besserve, M. Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism 53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[BibTex]

Independence of cause and mechanism in brain networks

Besserve, M.

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

[BibTex]

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

[BibTex]

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]

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]

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]

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J. Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI World Molecular Imaging Conference, 2015 (talk)

[BibTex]

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]

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J. 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 World Molecular Imaging Conference, 2015 (talk)

[BibTex]

Cosmology from Cosmic Shear with DES Science Verification Data

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others,

arXiv preprint arXiv:1507.05552, 2015 (techreport)

link (url) [BibTex]

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others, Cosmology from Cosmic Shear with DES Science Verification Data arXiv preprint arXiv:1507.05552, 2015 (techreport)

link (url) [BibTex]

The DES Science Verification Weak Lensing Shear Catalogs

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others,

arXiv preprint arXiv:1507.05603, 2015 (techreport)

link (url) [BibTex]

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others, The DES Science Verification Weak Lensing Shear Catalogs arXiv preprint arXiv:1507.05603, 2015 (techreport)

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]

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)

Web [BibTex]

2013

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)

Web [BibTex]

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)

[BibTex]

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)

[BibTex]

Animating Samples from Gaussian Distributions

Hennig, P.

(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

PDF [BibTex]

Hennig, P. Animating Samples from Gaussian Distributions (8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

PDF [BibTex]

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)

Web [BibTex]

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)

Web [BibTex]

Domain Generalization via Invariant Feature Representation

Muandet, K.

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

PDF [BibTex]

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

PDF [BibTex]

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

Lopez-Paz, D., Duvenaud, D.

2013 (talk)

PDF Web [BibTex]

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

PDF Web [BibTex]

Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era

Hogg, D. W., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Lang, D., Montet, B. T., Schiminovich, D., Schölkopf, B.

arXiv:1309.0653, 2013 (techreport)

link (url) [BibTex]

Hogg, D. W., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Lang, D., Montet, B. T., Schiminovich, D., Schölkopf, B. Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era arXiv:1309.0653, 2013 (techreport)

link (url) [BibTex]

Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars

Montet, B. T., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Hogg, D. W., Lang, D., Schiminovich, D., Schölkopf, B.

arXiv:1309.0654, 2013 (techreport)

link (url) [BibTex]

Montet, B. T., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Hogg, D. W., Lang, D., Schiminovich, D., Schölkopf, B. Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars arXiv:1309.0654, 2013 (techreport)

link (url) [BibTex]

Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik

Schölkopf, B., Luo, Z., Vovk, V.

Springer, 2013 (book)

[BibTex]

Schölkopf, B., Luo, Z., Vovk, V. Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik Springer, 2013 (book)

[BibTex]

2002

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Schölkopf, B., Smola, A.

pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Abstract

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs-kernelsfor a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Web [BibTex]

2002

Schölkopf, B., Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Web [BibTex]

Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.

(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

Abstract

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.

PDF [BibTex]

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V. Kernel Dependency Estimation (98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

PDF [BibTex]

Global Geometry of SVM Classifiers

Zhou, D., Xiao, B., Zhou, H., Dai, R.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

Abstract

We construct an geometry framework for any norm Support Vector Machine (SVM) classifiers. Within this framework, separating hyperplanes, dual descriptions and solutions of SVM classifiers are constructed by a purely geometric fashion. In contrast with the optimization theory used in SVM classifiers, we have no complicated computations any more. Each step in our theory is guided by elegant geometric intuitions.

PDF PostScript [BibTex]

Zhou, D., Xiao, B., Zhou, H., Dai, R. Global Geometry of SVM Classifiers Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

PDF PostScript [BibTex]

Computationally Efficient Face Detection

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

(MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

Web [BibTex]

Romdhani, S., Torr, P., Schölkopf, B., Blake, A. Computationally Efficient Face Detection (MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

Web [BibTex]

Kernel-based nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

EU-Project BLISS, January 2002 (techreport)

GZIP [BibTex]

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K. Kernel-based nonlinear blind source separation EU-Project BLISS, January 2002 (techreport)

GZIP [BibTex]

Transductive Learning: Motivation, Models, Algorithms

Bousquet, O.

January 2002 (talk)

PDF [BibTex]

Bousquet, O. Transductive Learning: Motivation, Models, Algorithms January 2002 (talk)

PDF [BibTex]

A compression approach to support vector model selection

von Luxburg, U., Bousquet, O., Schölkopf, B.

(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

Abstract

In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct ``compression coefficients'' for SVMs, which measure the amount by which the training labels can be compressed by some classification hypothesis. The main idea is to relate the coding precision of this hypothesis to the width of the margin of the SVM. The compression coefficients connect well known quantities such as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel matrix and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on several real world datasets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.

[BibTex]

von Luxburg, U., Bousquet, O., Schölkopf, B. A compression approach to support vector model selection (101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

[BibTex]

Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Observations on the Nyström Method for Gaussian Process Prediction

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

Abstract

A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nystr{\"o}m method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nystr{\"o}m method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nystr{\"o}m approximation would be expected to provide a good approximation to exact GP regression.

PostScript [BibTex]

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V. Observations on the Nyström Method for Gaussian Process Prediction Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

PostScript [BibTex]

2001

Kernel Methods for Extracting Local Image Semantics

Bradshaw, B., Schölkopf, B., Platt, J.

(MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Web [BibTex]

2001

Bradshaw, B., Schölkopf, B., Platt, J. Kernel Methods for Extracting Local Image Semantics (MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Web [BibTex]

Calibration of Digital Amateur Cameras

Urbanek, M., Horaud, R., Sturm, P.

(RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]

Urbanek, M., Horaud, R., Sturm, P. Calibration of Digital Amateur Cameras (RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]

Use of the ell_0-norm with linear models and kernel methods

Weston, J., Elisseeff, A., Schölkopf, B.

Biowulf Technologies, 2001 (techreport)

[BibTex]

Weston, J., Elisseeff, A., Schölkopf, B. Use of the ell_0-norm with linear models and kernel methods Biowulf Technologies, 2001 (techreport)

[BibTex]

KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

BIOwulf, 2001 (techreport)

Web [BibTex]

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin BIOwulf, 2001 (techreport)

Web [BibTex]

Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O., Schölkopf, B.

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

Abstract

We consider the problem of how to incorporate in the Support Vector Machine (SVM) framework invariances given by some a priori known transformations under which the data should be invariant. It extends some previous work which was only applicable with linear SVMs and we show on a digit recognition task that the proposed approach is superior to the traditional Virtual Support Vector method.

PostScript [BibTex]

Chapelle, O., Schölkopf, B. Incorporating Invariances in Non-Linear Support Vector Machines Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

PostScript [BibTex]

Data cleaning algorithms with applications to micro-array experiments

Weston, J., Chapelle, O., Guyon, I.

Biowulf, 2001 (techreport)

PostScript [BibTex]

Weston, J., Chapelle, O., Guyon, I. Data cleaning algorithms with applications to micro-array experiments Biowulf, 2001 (techreport)

PostScript [BibTex]

Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs

Gretton, A., Herbrich, R., Schölkopf, B., Smola, A., Rayner, P.

University of Cambridge, 2001 (techreport)

[BibTex]

Gretton, A., Herbrich, R., Schölkopf, B., Smola, A., Rayner, P. Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs University of Cambridge, 2001 (techreport)

[BibTex]

Bound on the Leave-One-Out Error for 2-Class Classification using nu-SVMs

Gretton, A., Herbrich, R., Schölkopf, B., Rayner, P.

University of Cambridge, 2001, Updated May 2003 (literature review expanded) (techreport)

Abstract

Three estimates of the leave-one-out error for $nu$-support vector (SV) machine binary classifiers are presented. Two of the estimates are based on the geometrical concept of the {em span}, which was introduced in the context of bounding the leave-one-out error for $C$-SV machine binary classifiers, while the third is based on optimisation over the criterion used to train the $nu$-support vector classifier. It is shown that the estimates presented herein provide informative and efficient approximations of the generalisation behaviour, in both a toy example and benchmark data sets. The proof strategies in the $nu$-SV context are also compared with those used to derive leave-one-out error estimates in the $C$-SV case.

PostScript [BibTex]