Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

MPI Papers

MPI Papers

Publication Type

Article (743)
Book (19)
Book Chapter (97)
Conference Paper (1158)
Miscellaneous
Patent (27)
Poster (201)
Proceedings (14)
Talk (143)
Technical Report
Thesis (145)
Unpublished (1)

Year

2019 (60)
2018 (143)
2017 (124)
2016
2015 (126)
2014 (118)
2013 (132)
2012
2011 (169)
2010 (194)
2009 (178)
2008 (178)
2007 (190)
2006 (159)
2005 (129)
2004 (132)
2003 (116)
2002
2001
2000 (46)
1999 (31)
1998 (38)
1997 (14)
1996 (12)
1995 (6)
1994 (3)
1993 (2)

9 results (View BibTeX file of all listed publications)

2016

Special Issue on Causal Discovery and Inference

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.

ACM Transactions on Intelligent Systems and Technology (TIST), 7(2), January 2016, (Guest Editors) (misc)

[BibTex]

2016

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J. Special Issue on Causal Discovery and Inference ACM Transactions on Intelligent Systems and Technology (TIST), 7(2), January 2016, (Guest Editors) (misc)

[BibTex]

Empirical Inference (2010-2015)

Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

Empirical Inference (2010-2015) Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

A note on the expected minimum error probability in equientropic channels

Weichwald, S., Fomina, T., Schölkopf, B., Grosse-Wentrup, M.

2016 (misc)

PDF Arxiv [BibTex]

Weichwald, S., Fomina, T., Schölkopf, B., Grosse-Wentrup, M. A note on the expected minimum error probability in equientropic channels 2016 (misc)

PDF Arxiv [BibTex]

Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set

Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T.

2016 (misc)

Arxiv [BibTex]

Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T. Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set 2016 (misc)

Arxiv [BibTex]

Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws?

Veiga, F., Peters, J.

2016 (misc)

arXiv [BibTex]

Veiga, F., Peters, J. Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws? 2016 (misc)

arXiv [BibTex]

2012

High Gamma-Power Predicts Performance in Brain-Computer Interfacing

Grosse-Wentrup, M., Schölkopf, B.

(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

Abstract

Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this nding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.

PDF [BibTex]

2012

Grosse-Wentrup, M., Schölkopf, B. High Gamma-Power Predicts Performance in Brain-Computer Interfacing (3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

PDF [BibTex]

2002

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]

2002

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]

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]