48 results
(View BibTeX file of all listed publications)

**Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI)**
pages: 869, AUAI Press, June 2016 (proceedings)

**Nonlinear functional causal models for distinguishing cause from effect**
In *Statistics and Causality: Methods for Applied Empirical Research*, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)

**A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis**
In *Brain-Computer Interfaces: Lab Experiments to Real-World Applications*, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)

**Analysis of multiparametric MRI using a semi-supervised random forest framework allows the detection of therapy response in ischemic stroke**
World Molecular Imaging Conference, 2016 (talk)

**Screening Rules for Convex Problems**
2016 (unpublished) Submitted

**Multi-view learning on multiparametric PET/MRI quantifies intratumoral heterogeneity and determines therapy efficacy**
World Molecular Imaging Conference, 2016 (talk)

**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)

**A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)**
In *Brain-Computer Interface Research*, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

**Semi-supervised learning in causal and anticausal settings**
In *Empirical Inference*, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

**Tractable large-scale optimization in machine learning**
In *Tractability: Practical Approaches to Hard Problems*, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

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

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

**On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension**
In *Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik*, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

**Support Vector Machines, Support Measure Machines, and Quasar Target Selection**
Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

**Hilbert Space Embedding for Dirichlet Process Mixtures**
NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

**Simultaneous small animal PET/MR in activated and resting state reveals multiple brain networks**
20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

**A new PET insert for simultaneous PET/MR small animal imaging**
20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

**Evaluation of a new, large field of view, small animal PET/MR system**
50. Jahrestagung der Deutschen Gesellschaft fuer Nuklearmedizin (NuklearMedizin), April 2012 (talk)

**Expectation-Maximization methods for solving (PO)MDPs and optimal control problems**
In *Inference and Learning in Dynamic Models*, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

**Active Learning Methods in Classification of Remote Sensing Images**
In *Signal and Image Processing for Remote Sensing*, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

Muandet, K.
**Support Measure Machines for Quasar Target Selection**
Astro Imaging Workshop, 2012 (talk)

**PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics
**
Workshop on Statistical Physics of Inference and Control Theory, 2012 (talk)

**PET Performance Measurements of a Next Generation Dedicated Small Animal PET/MR Scanner**
Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), 2012 (talk)

**Inferential structure determination from NMR data**
In *Bayesian methods in structural bioinformatics*, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

**Simultaneous small animal PET/MR reveals different brain networks during stimulation and rest**
World Molecular Imaging Congress (WMIC), 2012 (talk)

**Robot Learning**
In *Encyclopedia of the sciences of learning*, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

**Reinforcement Learning in Robotics: A Survey**
In *Reinforcement Learning*, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

**PAC-Bayesian Analysis of Supervised, Unsupervised, and Reinforcement Learning
**
Tutorial at the 29th International Conference on Machine Learning (ICML), 2012 (talk)

**Influence of MR-based attenuation correction on lesions within bone and susceptibility artifact regions**
Molekulare Bildgebung (MoBi), 2012 (talk)

**Structured Apprenticeship Learning**
European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

**PAC-Bayesian Analysis and Its Applications**
Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (talk)

**Machine Learning and Interpretation in Neuroimaging - Revised Selected and Invited Contributions**
pages: 266, Springer, Heidelberg, Germany, International Workshop, MLINI, Held at NIPS, 2012, Lecture Notes in Computer Science, Vol. 7263 (proceedings)

**Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise
**
European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

**Kernel Bellman Equations in POMDPs**
Technical Committee on Infomation-Based Induction Sciences and Machine Learning (IBISML'12), 2012 (talk)

**Higher-Order Tensors in Diffusion MRI**
In *Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data*, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

**MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication)
**
15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI , 2012 (proceedings)

**Beta oscillations propagate as traveling waves in the macaque prefrontal cortex**
42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (talk)

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

**Robust ensemble learning**
In *Advances in Large Margin Classifiers*, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

**Entropy numbers for convex combinations and MLPs**
In *Advances in Large Margin Classifiers*, pages: 369-387, Neural Information Processing Series, (Editors: AJ Smola and PL Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA,, October 2000 (inbook)

**Natural Regularization from Generative Models**
In *Advances in Large Margin Classifiers*, pages: 51-60, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

**Solving Satisfiability Problems with Genetic Algorithms**
In *Genetic Algorithms and Genetic Programming at Stanford 2000*, pages: 206-213, (Editors: Koza, J. R.), Stanford Bookstore, Stanford, CA, USA, June 2000 (inbook)

**Statistical Learning and Kernel Methods**
In *CISM Courses and Lectures, International Centre for Mechanical Sciences Vol.431*, *CISM Courses and Lectures, International Centre for
Mechanical Sciences*, 431(23):3-24, (Editors: G Della Riccia and H-J Lenz and R Kruse), Springer, Vienna, Data Fusion and Perception, 2000 (inbook)

**An Introduction to Kernel-Based Learning Algorithms**
In *Handbook of Neural Network Signal Processing*, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (inbook)

**Kernel principal component analysis.**
In *Advances in Kernel Methods—Support Vector Learning*, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

**Entropy numbers, operators and support vector kernels.**
In *Advances in Kernel Methods - Support Vector Learning*, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)