34 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)

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

**Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**Positional Oligomer Importance Matrices**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**Machine Learning Algorithms for Polymorphism Detection**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**An Automated Combination of Kernels for Predicting Protein Subcellular Localization**
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

**Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration**
Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

**Policy Learning for Robotics**
14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

**Hilbert Space Representations of Probability Distributions**
2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

**Regression with Intervals**
International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

**MR-Based PET Attenuation Correction: Method and Validation**
Joint Molecular Imaging Conference, September 2007 (talk)

**Predicting Structured Data**
pages: 360, Advances in neural information processing systems, MIT Press, Cambridge, MA, USA, September 2007 (book)

**Bayesian methods for NMR structure determination**
29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

**Collaborative Filtering via Ensembles of Matrix Factorizations**
KDD Cup and Workshop, August 2007 (talk)

**Thinking Out Loud: Research and Development of Brain Computer Interfaces**
Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

**Local Learning Algorithms for Transductive Classification, Clustering and Data Projection**
Yahoo Inc., July 2007 (talk)

**Dirichlet Process Mixtures of Factor Analysers**
Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

**New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams**
Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

**Towards Motor Skill Learning in Robotics**
Interactive Robot Learning - RSS workshop, June 2007 (talk)

**Transductive Support Vector Machines for Structured Variables**
International Conference on Machine Learning (ICML), June 2007 (talk)

**Impact of target-to-target interval on classification performance in the P300 speller**
Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

**New Margin- and Evidence-Based Approaches for EEG Signal Classification**
Invited talk at the FaSor Jahressymposium, February 2007 (talk)

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

ei
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)

**Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking**
January 2004 (talk)

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

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