All Publications

Journal/Magazine Papers

Dino Oglic, Steven A. Oatley, Simon J. F. Macdonald, Thomas Mcinally, Roman Garnett, Jonathan D. Hirst, and Thomas Gärtner. Active search for computer-aided drug design. Molecular Informatics, 2018.
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.
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.
Hanna Geppert, Tamás Horváth, Thomas Gärtner, Stefan Wrobel, and Jürgen Bajorath. Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds. Journal of Chemical Information and Modeling, 2008.
Kurt Driessens, Jan Ramon, and Thomas Gärtner. Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. Machine Learning, 2006. [ @springer ]
Thomas Gärtner, John W. Lloyd, and Peter A. Flach. Kernels and Distances for Structured Data. Machine Learning, 2004. [ @springer ]
Thomas Gärtner. A Survey of Kernels for Structured Data. SIGKDD Explorations, 2003. [ @portal.acm ]

Conference Papers

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. Accepted for publication in Advances in Neural Information Processing Systems 30, 2017.
Katrin Ullrich, Michael Kamp, Thomas Gärtner, Martin Vogt, and Stefan Wrobel. Co-Regularised Support Vector Regression. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, 2017.
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.
Michael Kamp, Mario Boley, Thomas Gärtner. Beating Human Analysts in Nowcasting Corporate Earnings by using Publicly Available Stock Price and Correlation Features. Proceedings of the SIAM International Conference on Data Mining, (72):641-649, 2014.
Dino Oglic, Daniel Paurat, Thomas Gärtner. Interactive Knowledge-Based Kernel PCA. Proceedings of ECML PKDD 2014, 2014. Springer.
Daniel Paurat, Thomas Gärtner. InVis: A Tool for Interactive Visual Data Analysis. Proceedings of ECML PKDD 2013, 2013.
Mario Boley, Sandy Moens, 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.
Mario Boley, Claudio Lucchese, Daniel Paurat, 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, 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.
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 ]
Sebastian Bothe, Thomas Gärtner, Stefan Wrobel. On-line Handwriting Recognition with Parallelized Machine Learning Algorithms. In Proceedings of the 33rd Annual German Conference on Artificial Intelligence (KI 2010), volume 6359 of Lecture Notes in Computer Science, page 82-90. 2010. Springer.
Mario Boley, Henrik Grosskreutz, Thomas Gärtner. Formal Concept Sampling for Counting and Threshold-Free Local Pattern Mining. Proc. of the SIAM Int. Conf. on Data Mining (SDM 2010), 2010. SIAM. [ electr.edt. ]
Hongqi Wang, Olana Missura, Thomas Gärtner, Stefan Wrobel. Context-Based Clustering of Image Search Results. In Bärbel Mertsching, Marcus Hund, Muhammad Zaheer Aziz, editor(s), KI, volume 5803 of Lecture Notes in Computer Science, page 153-160. 2009. Springer.
Shankar Vembu, Thomas Gärtner, Mario Boley. Probabilistic Structured Predictors. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI), 2009.
Olana Missura, Thomas Gärtner. Player Modeling for Intelligent Difficulty Adjustment. In Joao Gama, Vitor Santos Costa, Alipio Mario Jorge, Pavel Brazdil, editor(s), Discovery Science, volume 5808 of Lecture Notes in Computer Science, page 197-211. 2009. Springer.
Mario Boley, Thomas Gärtner. On the Complexity of Constraint-Based Theory Extraction. Discovery Science, 12th International Conference, DS 2009, Porto, Portugal, volume 5808 of LNCS, page 92-106. 2009. Springer.
Karina Zapien, Thomas Gärtner, Gilles Gasso, Stéphane Canu. Regularization path for ranking svm. Proceedings of the 11th European Symposium on Artificial Neural Networks, April 2008.
Olana Missura, Thomas Gärtner. Online Adaptive Agent for Connect Four. Proceedings of the Fourth International Conference on Games Research and Development CyberGames 2008, page 1-8. 2008.
Thomas Gärtner and Gemma C. Garriga. The Cost of Learning Directed Cuts. Proceedings of the 18th European Conference on Machine Learning, 2007.
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 ]
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 ]
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 | BibTeX ]
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 ]
Kristian Kersting and Thomas Gärtner. Fisher Kernels for Logical Sequences. In Proceedings of the 15th European Conference on Machine Learning (ECML-04), volume 3201 of LNCS, 2004.
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, 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, Kurt Driessens, and Jan Ramon. Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. Proceedings of the 13th International Conference on Inductive Logic Programming, 2003. [ pdf ]
Thomas Gärtner, John W. Lloyd, and Peter A. Flach. Kernels for Structured Data. Proceedings of the 12th International Conference on Inductive Logic Programming, 2002. Springer-Verlag.
Kristian Kersting and Thomas Gärtner. Fisher Kernels and Logical Sequences with an Application to Protein Fold Recognition. NIPS Workshop on Machine Learning Techniques for Bioinformatics, 2002.
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.
Thomas Gärtner. Sicherheitsinfrastrukturen für Nachrichten mit benutzerbezogenen Email-Proxies. Sicherheitsinfrastrukturen, Vieweg, 1999.

Workshop Papers

Daniel Paurat, Roman Garnett, and Thomas Gärtner. Interactive Exploration of Larger Pattern Collections: A Case Study on a Cocktail Dataset. Proceedings of the 2nd ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, 2014.
Daniel Paurat, Roman Garnett, and Thomas Gärtner. Constructing Cocktails from a Cocktail Map. Proceedings of the NIPS 1st Workshop on Constructive Machine Learning, 2013.
Michael Kamp, Mario Boley, and Thomas Gärtner. Beating Human Analysts in Nowcasting Corporate Earnings by using Publicly Available Stock Price and Correlation Features. Proceedings of the Workshop on Domain Driven Data Mining (DDDM 2013) at ICDM 2013, 2013. IEEE.
Roman Garnett, Richard Mann, and Thomas Gärtner. Animal Foraging via Optimal Search. International Conference on Models in Population Dynamics and Ecology (MPDE 2013), 2013.
Daniel Paurat, Dino Oglic, and Thomas Gärtner. Supervised PCA for Interactive Data Analysis. Proceedings of the NIPS 2nd Workshop on Spectral Learning, 2013.
Mario Boley, Claudio Lucchese, Daniel Paurat, and Thomas Gärtner. Direct Pattern Sampling with Respect to Pattern Frequency*. Workshop on Knowledge Discovery, Data Mining, and Machine Learning (KDML 2011), Magdeburg, Germany, September 2011, 2011.
Olana Missura and Thomas Gärtner. Player Modeling for Intelligent Difficulty Adjustment. In Arno Knobbe, Johannes Fürnkranz, editor(s), Proceedings of the ECML-09 Workshop From Local Patterns to Global Models (LeGo-09), Bled, Slovenia, September 07 2009.
Shankar Vembu, Thomas Gärtner, and Mario Boley. MAP Structured Prediction by Sampling. 7th International Workshop on Mining and Learning with Graphs, 2009.
Olana Missura and Thomas Gärtner. Player Modeling for Intelligent Difficulty Adjustment. In Melanie Hartmann, Frederik Janssen, editor(s), LWA, Technical Report, page 76-83. 2009. Department of Computer Science, Technische Universität Darmstadt, Germany.
Olana Missura and Thomas Gärtner. Classification on Graphs for Dynamic Difficulty Adjustment. In H. Blockeel, K. Borgwardt, X. Yan, editor(s), Mining and Learning with Graphs, 2009.
Olana Missura, Kristian Kersting, and Thomas Gärtner. Towards Engaging Games. In Joachim Baumeister, Martin Atzmüller, editor(s), LWA, volume 448 of Technical Report, page 77-83. 2008. Department of Computer Science, University of Würzburg, Germany.
Thomas Gärtner and Shankar Vembu. Learning to Predict Combinatorial Structures. NIPS Workshop on Structured Inputs and Structured outputs, 2008. [ video ]
Thomas Gärtner and Shankar Vembu. Leveraging Matrix Computations for Efficient Learning on Graphs. KDD Workshop on Data Mining using Matrices and Tensors, 2008.
Shankar Vembu and Thomas Gärtner. Training Non-linear Structured Prediction Models with Stochastic Gradient Descent. 6th International Workshop on Mining and Learning with Graphs, 2008.
Shankar Vembu, Thomas Gärtner, and Stefan Wrobel. Semidefinite Ranking on Graphs. Proceedings of the 5th International Workshop on Mining and Learning with Graphs, 2007.
Shankar Vembu, Thomas Gärtner, and Stefan Wrobel. Semidefinite Ranking on Graphs. In Abstracts Booklet of the PASCAL Workshop on Graph Theory and Machine learning at the 6th Slovenian International Conference on Graph Theory, 2007. [ video ]
Thomas Gärtner. Abstract: Efficient Kernel Methods for Graphs. Proceedings of the 5th International Workshop on Mining and Learning with Graphs, 2007.
Thomas Gärtner and Gemma C. Garriga. Abstract: The Cost of Learning Directed Cuts. Proceedings of the 5th International Workshop on Mining and Learning with Graphs, 2007.
Thomas Gärtner, Gemma C. Garriga, and Thorsten Knopp, Peter A. Flach, Stefan Wrobel. Estimating the Joint AUC of Labelled and Unlabelled Data. Learning when test and training inputs have different distributions (Workshop at the NIPS*2006 conference), 2006.
Thomas Gärtner. Convergence of the MI Kernel Perceptron. The 2003 UK Workshop on Computational Intelligence, 2003.
Jan Ramon and Thomas Gärtner. Expressivity versus Efficiency of Graph Kernels. First International Workshop on Mining Graphs, Trees and Sequences, 2003.
Thomas Gärtner and Peter A. Flach. Kernel-Based Diterpene Structure Elucidation from 13C Nuclear Magnetic Resonance spectra. NIPS Workshop on Machine Learning Techniques for Bioinformatics, 2002.
Thomas Gärtner. Exponential and Geometric Kernels for Graphs. NIPS Workshop on Unreal Data: Principles of Modeling Nonvectorial Data, 2002.
A. Voß, Thomas Gärtner, and S. Moyle. Zeno for Rapid Collaboration in Data Mining Projects. IDDM-2001: Positions, Developments and Future Directions workshop, 2001.
Thomas Gärtner, S. Wu, and Peter A. Flach. Data Mining on the Sisyphus Dataset: Evaluation and Integration of Results. Integrating Aspects of Data Mining, Decision Support and Meta-Learning workshop, 2001.
Thomas Gärtner and Peter A. Flach. A Linear Kernel on Strongly Typed Terms. Multi-Relational Data Mining workshop, 2001.
Thomas Gärtner and Peter A. Flach. Multiple-Instance Kernels for Drug Activity Prediction. NIPS Workshop on New Directions in Kernel-Based Learning Methods, 2001.

Edited Volumes

Thomas Gärtner, Tamás Horváth, and Stefan Wrobel. Graph Kernels. In C. Sammut, Geoffrey I. Webb, editor(s), Encyclopedia of Machine Learning, page 467 - 469. Springer-Verlag, Heidelberg, 2010. [ WWW ]
Thomas Gärtner, Gemma C. Garriga, and Thorsten Meinl, editor(s). Proceedings of The Workshop on Mining and Learning with Graphs. 2006.

Book Chapters

Thomas Gärtner and Shankar Vembu. Label Ranking Algorithms: A Survey. In Johannes Fürnkranz, Eyke Hüllermeier, editor(s), Preference Learning, Springer-Verlag, 2010. (to appear).
Thomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel. Kernel Methods for Graphs. Mining Graph Data, 2007.
Thomas Gärtner. Kernfunktionen für Strukturierte Daten. Ausgezeichnete Informatikdissertationen 2005, 2006.
Stefan Wrobel, Thomas Gärtner, and Tamás Horváth. Kernels for Predictive Graph Mining. From Data and Information Analysis to Knowledge Engineering, 2006.
Thomas Gärtner. Predictive Graph Mining with Kernel Methods. Advanced Methods for Knowledge Discovery from Complex Data, 2005.
Peter A. Flach, H. Blockeel, Thomas Gärtner, M. Grobelnik, B. Kavsek, M. Kejkula, D. Krzywania, N. Lavrac, P. Ljubic, D. Mladenic, S. Moyle, S. Raeymaekers, J. Rauch, S. Rawles, R. Ribeiro, G. Sclep, J. Struyf, L. Todorovski, L. Torgo, D. Wettschereck, and S. Wu. On the road to knowledge: mining 21 years of UK traffic accident reports. Data Mining and Decision Support: Aspects of Integration and Collaboration, 2003.

Theses

Thomas Gärtner. Kernels for Structured Data. Universität Bonn, 2005. [ buy it from amazon.de, amazon.co.uk, or elsewhere ].