44 results
(View BibTeX file of all listed publications)

**ABCDP: Approximate Bayesian Computation Meets Differential Privacy **
2019 (misc) Submitted

**A Kernel Method for the Two-Sample-Problem**
20th Annual Conference on Neural Information Processing Systems (NIPS), December 2006 (talk)

**Ab-initio gene finding using machine learning**
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

**Reinforcement Learning by Reward-Weighted Regression**
NIPS Workshop: Towards a New Reinforcement Learning? , December 2006 (talk)

**Graph boosting for molecular QSAR analysis**
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

**Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions**
NIPS Workshop on Causality and Feature Selection, December 2006 (talk)

**Learning Optimal EEG Features Across Time, Frequency and Space**
NIPS Workshop on Current Trends in Brain-Computer Interfacing, December 2006 (talk)

**Semi-Supervised Learning**
Advanced Methods in Sequence Analysis Lectures, November 2006 (talk)

**A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images**
IEEE Medical Imaging Conference, November 2006 (talk)

**Semi-Supervised Support Vector Machines and Application to Spam Filtering**
ECML Discovery Challenge Workshop, September 2006 (talk)

**Semi-Supervised Learning**
pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

**Inferential Structure Determination: Probabilistic determination and validation of NMR structures**
Gordon Research Conference on Computational Aspects of Biomolecular
NMR, September 2006 (talk)

**Machine Learning Algorithms for Polymorphism Detection**
2nd ISCB Student Council Symposium, August 2006 (talk)

**Inferential structure determination: Overview and new developments**
Sixth CCPN Annual Conference: Efficient and Rapid Structure Determination by NMR, July 2006 (talk)

**MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models**
ICML Workshop on Learning with Nonparametric Bayesian Methods, June 2006 (talk)

**Sampling for non-conjugate infinite latent feature models**
(Editors: Bernardo, J. M.), 8th Valencia International Meeting on Bayesian Statistics (ISBA), June 2006 (talk)

**An Inventory of Sequence Polymorphisms For Arabidopsis**
17th International Conference on Arabidopsis Research, April 2006 (talk)

**Machine Learning and Applications in Biology**
6th Course in Bioinformatics for Molecular Biologist, March 2006 (talk)

**Gaussian Processes for Machine Learning**
pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

**Spectral clustering and transductive inference for graph data**
NIPS Workshop on Kernel Methods and Structured Domains, December 2005 (talk)

**Some thoughts about Gaussian Processes**
NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

Chapelle, O.
**A taxonomy of semi-supervised learning algorithms**
Yahoo!, December 2005 (talk)

**Building Sparse Large Margin Classifiers**
The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

**Learning from Labeled and Unlabeled Data on a Directed Graph**
The 22nd International Conference on Machine Learning, August 2005 (talk)

**Machine-Learning Approaches to BCI in Tübingen**
Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

**Learning Motor Primitives with Reinforcement Learning**
ROBOTICS Workshop on Modular Foundations for Control and Perception, June 2005 (talk)

**Motor Skill Learning for Humanoid Robots**
First Conference Undergraduate Computer Sciences and Informations Sciences (CS/IS), May 2005 (talk)

**Kernel Constrained Covariance for Dependence Measurement**
AISTATS, January 2005 (talk)

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)

**Statistische Lerntheorie und Empirische Inferenz**
*Jahrbuch der Max-Planck-Gesellschaft*, 2004, pages: 377-382, 2004 (misc)

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

**Advances in Large Margin Classifiers**
pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)

**Advances in Kernel Methods - Support Vector Learning**
MIT Press, Cambridge, MA, 1999 (book)

**Das Spiel mit dem künstlichen Leben.**
*Frankfurter Allgemeine Zeitung, Wissenschaftsbeilage*, June 1997 (misc)

**Support vector learning**
pages: 173, Oldenbourg, München, Germany, 1997, Zugl.: Berlin, Techn. Univ., Diss., 1997 (book)