43 results
(View BibTeX file of all listed publications)

**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)

**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)

**Single-Source Domain Adaptation with Target and Conditional Shift**
In *Regularization, Optimization, Kernels, and Support Vector Machines*, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

**Higher-Order Tensors in Diffusion Imaging**
In *Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data*, pages: 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (inbook)

**Fuzzy Fibers: Uncertainty in dMRI Tractography**
In *Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization*, pages: 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (inbook)

**Nonconvex Proximal Splitting with Computational Errors**
In *Regularization, Optimization, Kernels, and Support Vector Machines*, pages: 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (inbook)

**Active Learning - Modern Learning Theory**
In *Encyclopedia of Algorithms*, (Editors: Kao, M.-Y.), Springer Berlin Heidelberg, 2014 (incollection)

**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)

**Machine Learning for Brain-Computer Interfaces**
Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

**PAC-Bayesian Approach to Formulation of Clustering Objectives**
NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

**Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data**
Women in Machine Learning Workshop (WiML), December 2009 (talk)

**Event-Related Potentials in Brain-Computer Interfacing**
Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

**BCI2000 and Python**
Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

**Implementing a Signal Processing Filter in BCI2000 Using C++**
Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

**Toward a Theory of Consciousness**
In *The Cognitive Neurosciences*, pages: 1201-1220, (Editors: Gazzaniga, M.S.), MIT Press, Cambridge, MA, USA, October 2009 (inbook)

**Randomized algorithms for statistical image analysis based on percolation theory**
27th European Meeting of Statisticians (EMS), July 2009 (talk)

**Learning Motor Primitives for Robotics**
Advanced Telecommunications Research Center ATR, June 2009 (talk)

**Text Clustering with Mixture of von Mises-Fisher Distributions**
In *Text mining: classification, clustering, and applications*, pages: 121-161, Chapman & Hall/CRC data mining and knowledge discovery series, (Editors: Srivastava, A. N. and Sahami, M.), CRC Press, Boca Raton, FL, USA, June 2009 (inbook)

**Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer**
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

**Data Mining for Biologists**
In *Biological Data Mining in Protein Interaction Networks*, pages: 14-27, (Editors: Li, X. and Ng, S.-K.), Medical Information Science Reference, Hershey, PA, USA, May 2009 (inbook)

**Large Margin Methods for Part of Speech Tagging**
In *Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods*, pages: 141-160, (Editors: Keshet, J. and Bengio, S.), Wiley, Hoboken, NJ, USA, January 2009 (inbook)

**Covariate shift and local learning by distribution matching**
In *Dataset Shift in Machine Learning*, pages: 131-160, (Editors: Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, N. D.), MIT Press, Cambridge, MA, USA, 2009 (inbook)

**An introduction to Kernel Learning Algorithms**
In *Kernel Methods for Remote Sensing Data Analysis*, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)

**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)

**Extracting egomotion from optic flow: limits of accuracy and neural matched filters**
In pages: 143-168, Springer, Berlin, 2001 (inbook)