64 results
(View BibTeX file of all listed publications)

**Robot Learning**
In *Springer Handbook of Robotics*, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

**Policy Gradient Methods**
In *Encyclopedia of Machine Learning and Data Mining*, pages: 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

**Unsupervised clustering of EOG as a viable substitute for optical eye-tracking**
In *First Workshop on Eye Tracking and Visualization (ETVIS 2015)*, pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)

**Statistical Asymmetries Between Cause and Effect**
In *Time in Physics*, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)

**Robot Learning**
In *Encyclopedia of Machine Learning and Data Mining*, pages: 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

**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)

**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)

**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)

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

**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)

**Kernel methods in medical imaging**
In *Handbook of Biomedical Imaging*, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

**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)

**Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data**
In *Visualization and Processing of Higher Order Descriptors for Multi-Valued Data*, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

**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)

**Justifying Information-Geometric Causal Inference**
In *Measures of Complexity: Festschrift for Alexey Chervonenkis*, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

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

**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)

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)

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

**Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis**
In Volume 1: Cellular and Molecular Tools, pages: 109-128, (Editors: Sabatini, M., P. Pastoureau and F. De Ceuninck), Humana Press, July 2004 (inbook)

**Distributed Command Execution**
In *BSD Hacks: 100 industrial-strength tips & tools*, pages: 152-152, (Editors: Lavigne, Dru), O’Reilly, Beijing, May 2004 (inbook)

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

**Local Alignment Kernels for Biological Sequences**
In *Kernel Methods in Computational Biology*, pages: 131-153, MIT Press, Cambridge, MA,, 2004 (inbook)

**Gaussian Processes in Machine Learning**
In 3176, pages: 63-71, Lecture Notes in Computer Science, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, 2004, Copyright by Springer (inbook)

**Protein Classification via Kernel Matrix Completion**
In pages: 261-274, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

**Introduction to Statistical Learning Theory**
In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

**A Primer on Kernel Methods**
In *Kernel Methods in Computational Biology*, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

**Concentration Inequalities**
In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

**Kernels for graphs**
In pages: 155-170, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

**A primer on molecular biology**
In pages: 3-34, (Editors: Schoelkopf, B., K. Tsuda and J. P. Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

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)