26 results
(View BibTeX file of all listed publications)

**Advances in computational imaging: Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields**
Eberhard Karls Universität Tübingen, Germany, 2016 (phdthesis)

**Tractable Structured Prediction using the Permutohedral Lattice**
ETH Zurich, 2016 (phdthesis)

**Screening Rules for Convex Problems**
2016 (unpublished) Submitted

**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)

**Extension to Kernel Dependency Estimation with Applications to Robotics**
Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

**Geometrical aspects of statistical learning theory**
Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

**Implicit Surfaces For Modelling
Human Heads**
Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

**Machine Learning Methods for Brain-Computer Interdaces**
Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

**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)

**Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain**
Biologische Kybernetik, Eberhard-Karls University Tübingen, Tübingen, Germany, May 2005 (diplomathesis)

**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)

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

**Statistical Learning with Similarity and Dissimilarity Functions**
pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

**Classification and Feature Extraction in Man and Machine**
Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

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)