Papers

Some selected publications are listed below, I am trying to maintain a more or less complete list [@here]. It is, however, rather likely that my profile [@scholar] is more up to date. You can also look [@DBLP], [@springer], [@microsoft academic search], or [@acm]. Please also see the main page for newer items that didn't make it into this list yet.

Dino Oglic and Thomas Gärtner. Scalable Learning in Reproducing Kernel Kreı̆n Spaces. In Proceedings of the 36th International Conference on Machine learning, 2019.
Dino Oglic and Thomas Gärtner. Learning in Reproducing Kernel Kreı̆n Spaces. In Proceedings of the 35th International Conference on Machine learning, 2018.
Michael Kamp, Mario Boley, Olana Missura, and Thomas Gärtner. Effective Parallelisation for Machine Learning. Advances in Neural Information Processing Systems 30, 2017. [ video ]
Dino Oglic and Thomas Gärtner. Nyström method with Kernel K-means++ samples as landmarks. In Proceedings of the Thirty-Fourth 34th International Conference on Machine Learning, 2017.
Dino Oglic, Roman Garnett, and Thomas Gärtner. Active search in intensionally specified structured spaces. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017.
Dino Oglic and Thomas Gärtner. Greedy Feature Construction. In Advances in Neural Information Processing Systems 29, 2016.
Roman Garnett, Thomas Gärtner, Martin Vogt, and Jürgen Bajorath. Introducing the ‘active search’ method for iterative virtual screening. Journal of Computer-Aided Molecular Design, 2015.
Dino Oglic, Daniel Paurat, and Thomas Gärtner. Interactive Knowledge-Based Kernel PCA. Proceedings of ECML PKDD, 2014. Springer.
Daniel Paurat, and Thomas Gärtner. InVis: A Tool for Interactive Visual Data Analysis. Proceedings of ECML PKDD, 2013. Springer. [ InVis website with demovideo, manual, software ]
Mario Boley, Sandy Moens, and Thomas Gärtner. Linear Space Direct Pattern Sampling using Coupling From The Past. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, page 69-77. 2012. ACM.
Olana Missura, Thomas Gärtner. Predicting Dynamic Difficulty. In J. Shawe-Taylor, R.S. Zemel, P. Bartlett, F.C.N. Pereira, K.Q. Weinberger, editor(s), Advances in Neural Information Processing Systems 24, page 2007-2015. 2011. [ @nips ]
Mario Boley, Claudio Lucchese, Daniel Paurat, and Thomas Gärtner. Direct Local Pattern Sampling by Efficient Two-Step Random Procedures. The 17th annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
Jens Humrich, Thomas Gärtner, and Gemma C. Garriga. A Fixed Parameter Tractable Integer Program for Finding the Maximum Order Preserving Submatrix. 11th IEEE International Conference on Data Mining, ICDM, 2011.
Hanna Geppert, Jens Humrich, Dagmar Stumpfe, Thomas Gärtner, and Jürgen Bajorath. Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors. Journal of Chemical Information and Modeling, 49(4):767-779, 2009.
Thomas Gärtner and Shankar Vembu. On Structured Output Training: Hard Cases and an Efficient Alternative. Machine Learning Journal (Special Issue of ECML PKDD), 76(2):227–242, 2009.
Shankar Vembu, Thomas Gärtner, and Mario Boley. Probabilistic Structured Predictors. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI), 2009.
Quoc V. Le, Alex J. Smola, Thomas Gärtner, and Yasemin Altun. Transductive Gaussian Process Regression with Automatic Model Selection. Proceedings of the 17th European Conference on Machine Learning, 2006. Springer-Verlag. [ @springer ]<
Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, and S. V. N. Vishwanathan. Large-Scale Multiclass Transduction. Advances in Neural Information Processing Systems 18, 2006. [ @books.nips ]
Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, and Stefan Wrobel. Efficient Co-Regularised Least Squares Regression. Proceedings of the 23rd International Conference on Machine Learning, 2006. ACM Press. [ @imls ]
Quoc V. Le, Alex J. Smola, and Thomas Gärtner. Simpler Knowledge-based Support Vector Machines. Proceedings of the 23rd International Conference on Machine Learning, 2006. ACM Press. [ @imls ]
Kurt Driessens, Jan Ramon, and Thomas Gärtner. Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. Machine Learning, 2006. [ @springer ]
Thomas Gärtner. Kernels for Structured Data. Universität Bonn, 2005. [ buy it from amazon.de, amazon.co.uk, or elsewhere ].
Tamás Horváth, Thomas Gärtner, and Stefan Wrobel. Cyclic Pattern Kernels for Predictive Graph Mining. Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2004. [ @portal.acm ]
Thomas Gärtner, John W. Lloyd, and Peter A. Flach. Kernels and Distances for Structured Data. Machine Learning, 2004. [ @springer ]
Thomas Gärtner, Peter A. Flach, and Stefan Wrobel. On Graph Kernels: Hardness Results and Efficient Alternatives. Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop, 2003. [ @springer ]
Thomas Gärtner. A Survey of Kernels for Structured Data. SIGKDD Explorations, 2003. [ @portal.acm ]
Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, and Alex J. Smola. Multi-Instance Kernels. Proceedings of the 19th International Conference on Machine Learning, 2002. Morgan Kaufmann.
Thomas Gärtner and Peter A. Flach. WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. Proceedings of the 18th International Conference on Machine Learning, 2001. Morgan Kaufmann.

(sorry, due to a server crash and reset, papers aren't linked anymore at the moment.)