46 results
(View BibTeX file of all listed publications)

**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)

**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 **
18th Conference on Quantum Information Processing (QIP), 2015 (talk)

**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)

**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)

**The search for single exoplanet transits in the Kepler light curves**
*IAU General Assembly*, 22, pages: 2258352, 2015 (talk)

**Learning Motor Skills: From Algorithms to Robot Experiments**
97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

**Computational Diffusion MRI and Brain Connectivity**
pages: 255, Mathematics and Visualization, Springer, 2014 (book)

**Unsupervised identification of neural events in local field potentials**
44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)

**Quantifying statistical dependency**
Research Network on Learning Systems Summer School, 2014 (talk)

**Using a population based Gaussian Mixture Model on fused [18]F-FDG PET and DW-MRI images accurately segments the tumor microenvironment into clinically relevant compartments capable of guiding therapy**
European Molecular Imaging Meeting, 2014 (talk)

**Causal Inference from Passive Observations**
24th Summer School University of Jyväskylā, Finland, August, 2014 (talk)

**Combined whole-body PET/MR imaging: MR contrast agents do not affect the quantitative accuracy of PET following attenuation correction**
(SST15-05 ), 97th Scientific Assemble and Annual Meeting of the Radiological Society of North America (RSNA), December 2011 (talk)

**Optimization for Machine Learning**
pages: 494, Neural information processing series, MIT Press, Cambridge, MA, USA, December 2011 (book)

**Cooperative Cuts: a new use of submodularity in image segmentation**
Second I.S.T. Austria Symposium on Computer Vision and Machine Learning, October 2011 (talk)

**Effect of MR Contrast Agents on Quantitative Accuracy of PET in Combined Whole-Body PET/MR Imaging**
2011(MIC3-3), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

**First Results on Patients and Phantoms of a Fully Integrated Clinical Whole-Body PET/MRI**
2011(J2-8), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

**Effect of MR contrast agents on quantitative accuracy of PET in combined whole-body PET/MR imaging**
(OP314), Annual Congress of the European Association of Nuclear Medicine (EANM), October 2011 (talk)

**Multi-parametric Tumor Characterization and Therapy Monitoring using Simultaneous PET/MRI: initial results for Lung Cancer and GvHD**
(T110), 2011 World Molecular Imaging Congress (WMIC), September 2011 (talk)

**Statistical Image Analysis and Percolation Theory **
2011 Joint Statistical Meetings (JSM), August 2011 (talk)

**Bayesian Time Series Models**
pages: 432, Cambridge University Press, Cambridge, UK, August 2011 (book)

**Cooperative Cuts**
COSA Workshop: Combinatorial Optimization, Statistics, and Applications, March 2011 (talk)

**Handbook of Statistical Bioinformatics**
pages: 627, Springer Handbooks of Computational Statistics, Springer, Berlin, Germany, 2011 (book)

Zhou, D.
**How to learn from very few examples?**
October 2004 (talk)

Zhou, D.
**Discrete vs. Continuous: Two Sides of Machine Learning**
October 2004 (talk)

Zhou, D.
**Discrete vs. Continuous: Two Sides of Machine Learning**
October 2004 (talk)

**Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung**
September 2004 (talk)

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

**The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative
comparative study**
The thirteenth European Microscopy Congress, August 2004 (talk)

Bousquet, O.
**Introduction to Category Theory**
Internal Seminar, January 2004 (talk)

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

**Learning Control and Planning from the View of Control Theory and Imitation**
NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

**Recurrent neural networks from learning attractor dynamics**
NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

Bousquet, O.
**Statistical Learning Theory**
Machine Learning Summer School, August 2003 (talk)

**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)

Bousquet, O., Schölkopf, B.
**Statistical Learning Theory**
March 2003 (talk)

**Concentration Inequalities and Data-Dependent Error Bounds**
Uni. Jena, February 2003 (talk)

**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)

**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)

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

**Support Vector methods in learning and feature extraction**
*Ninth Australian Conference on Neural Networks*, pages: 72-78, (Editors: T. Downs, M. Frean and M. Gallagher), 1998 (talk)