11 results
(View BibTeX file of all listed publications)

**Elements of Causal Inference - Foundations and Learning Algorithms**
Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

**New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)**
*Dagstuhl Reports*, 6(11):142-167, 2017 (book)

**Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik**
Springer, 2013 (book)

**Machine Learning for Robotics: Learning Methods for Robot Motor Skills**
pages: 107 , (Editors: J Peters), VDM-Verlag, Saarbrücken, Germany, May 2008 (book)

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

**Large-Scale Kernel Machines**
pages: 416, Neural Information Processing Series, MIT Press, Cambridge, MA, USA, September 2007 (book)

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

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

**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)

**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)