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Thomas Gärtner

Professor of Data Science, Faculty of Science

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Biography

Thomas Gärtner joined the School of Computer Science in July 2015 as Professor of Data Science. Before that he was leading a research group jointly hosted by the University of Bonn and the Fraunhofer Institute for Intelligent Analysis and Information Systems. During that time he received an award from the Emmy Noether-Programme of the German Research Foundation (DFG). During his MSc studies at the University of Bristol and his PhD studies at the University of Bonn, Thomas focused on kernel functions for structured data in order to bridge the gap between theoretically sound machine learning algorithms and their real-world applications. His book, Kernels for Structured Data, was published in 2008. He has been an editor of the Machine Learning journal for several years and served as a senior programme committee member for several international flagship conferences on machine learning and data mining. Currently he is co-organizing the leading European conference on machine learning and data mining.

Research Summary

My main research interests are efficient and effective machine learning and data mining algorithms. Machine learning considers the problem of extracting useful functional or probabilistic… read more

Recent Publications

  • DINO OGLIC, ROMAN GARNETT and THOMAS GÄRTNER, 2017. Active Search in Intensionally Specified Structured Spaces In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017).
  • DINO OGLIC and THOMAS GÄRTNER, 2016. Greedy Feature Construction. In: Advances in Neural Information Processing Systems 29 (NIPS 2016)
  • K. ULLRICH, M. KAMP, T. GÄRTNER, M. VOGT and S. WROBEL, 2016. Ligand-Based Virtual Screening with Co-regularised Support Vector Regression In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
  • GARNETT, ROMAN, THOMAS GÄRTNER, VOGT, MARTIN and BAJORATH, JÜRGEN, 2015. Introducing the 'active search' method for iterative virtual screening Journal of Computer-Aided Molecular Design. 29(4),

Current Research

My main research interests are efficient and effective machine learning and data mining algorithms. Machine learning considers the problem of extracting useful functional or probabilistic dependencies from a sample of data. Such dependencies can then, for instance, be used to predict properties of partially observed data. Data mining is often used in a broader sense and includes several different computational problems, for instance, finding regularites or patterns in data. By efficiency I mean on the one hand the classical computational complexity of decision, enumeration, etc problems but on the other hand also a satisfactory response time that allows for effectiveness. By effectiveness I mean how well an algorithm helps to solve a real world problem.

My recent focus is on challenges relevant to the constructive machine learning setting where the task is to find domain instances with desired properties and the mapping between instances and their properties is only partially accessible. This includes structured output prediction, active learning/search, online learning/optimisation, knowledge-based learning and related areas. I am most interested in cases of this setting where at least one of the involved spaces is not a Euclidean space such as the set of graphs. My approach in many cases is based on kernel methods where I have focussed originally on kernels for structured data, moved to semi-supervised/transductive learning, and am currently looking at parallel/distributed approaches as well as fast approximations. The most recent knowledge-based kernel method was for instance focussing on interactive visualisations for data exploration. Application areas which I am often considering when looking for novel machine learning challenges are chemoinformatics and computer games.

  • DINO OGLIC, ROMAN GARNETT and THOMAS GÄRTNER, 2017. Active Search in Intensionally Specified Structured Spaces In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017).
  • DINO OGLIC and THOMAS GÄRTNER, 2016. Greedy Feature Construction. In: Advances in Neural Information Processing Systems 29 (NIPS 2016)
  • K. ULLRICH, M. KAMP, T. GÄRTNER, M. VOGT and S. WROBEL, 2016. Ligand-Based Virtual Screening with Co-regularised Support Vector Regression In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
  • GARNETT, ROMAN, THOMAS GÄRTNER, VOGT, MARTIN and BAJORATH, JÜRGEN, 2015. Introducing the 'active search' method for iterative virtual screening Journal of Computer-Aided Molecular Design. 29(4),
  • ROMAN GARNETT, THOMAS GÄRTNER, TIMOTHY ELLERSIEK, EYJOLFUR GUDMONDSSON and PETUR OSKARSSON, 2014. Predicting unexpected influxes of players in EVE online In: Conference on Computational Intelligence and Games.
  • MICHAEL KAMP, MARIO BOLEY and THOMAS GÄRTNER, 2014. Beating Human Analysts in Nowcasting Corporate Earnings by using Publicly Available Stock Price and Correlation Features In: Proceedings of the SIAM International Conference on Data Mining.
  • OGLIC, DINO, PAURAT, DANIEL and THOMAS GÄRTNER, 2014. Interactive Knowledge-Based Kernel PCA In: Machine Learning and Knowledge Discovery in Databases: European Conference.
  • DANIEL PAURAT and THOMAS GÄRTNER, 2013. InVis: A Tool for Interactive Visual Data Analysis In: Machine Learning and Knowledge Discovery in Databases: European Conference.
  • BOLEY, MARIO, MOENS, SANDY and THOMAS GÄRTNER, 2012. Linear Space Direct Pattern Sampling Using Coupling from the Past In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • OLANA MISSURA and THOMAS GÄRTNER, 2011. Predicting Dynamic Difficulty. In: Advances in Neural Information Processing Systems Curran Associates, Inc..
  • MARIO BOLEY, CLAUDIO LUCCHESE, DANIEL PAURAT and THOMAS GÄRTNER, 2011. Direct Local Pattern Sampling by Efficient Two-Step Random Procedures In: The 17th annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • THOMAS GÄRTNER, TAMAS HORVATH and STEFAN WROBEL, 2010. Graph Kernels. In: CLAUDE SAMMUT and GEOFFREY I. WEBB, eds., Encyclopedia of Machine Learning Springer.
  • THOMAS GÄRTNER, TAM'AS HORV'ATH and STEFAN WROBEL, 2009. Device and Method for Determining the Pharmaceutical Activity of a Molecule (PCT/EP2008/010779)
  • THOMAS GÄRTNER, 2009. Kernels For Structured Data World Scientific.
  • VEMBU, SHANKAR, THOMAS GÄRTNER and BOLEY, MARIO, 2009. Probabilistic Structured Predictors In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence.
  • HANNA GEPPERT, JENS HUMRICH, DAGMAR STUMPFE, THOMAS GÄRTNER and JÜRGEN BAJORATH, 2009. Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors Journal of Chemical Information and Modeling. 49(4),
  • HANNA GEPPERT, TAMAS HORVATH, THOMAS GÄRTNER, STEFAN WROBEL and JÜRGEN BAJORATH, 2008. 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.
  • THOMAS GÄRTNER and GEMMA C. GARRIGA, 2007. The Cost of Learning Directed Cuts In: Proceedings of the 18th European Conference on Machine Learning.
  • LE, QUOC V., SMOLA, ALEX J. and THOMAS GÄRTNER, 2006. Simpler Knowledge-based Support Vector Machines In: Proceedings of the 23rd International Conference on Machine Learning.
  • ULF BREFELD, THOMAS GÄRTNER, TOBIAS SCHEFFER and STEFAN WROBEL, 2006. Efficient Co-Regularised Least Squares Regression In: Proceedings of the 23rd International Conference on Machine Learning.
  • Q.~V.~LE, A.~J.~SMOLA, T.~GÄRTNER and Y.~ALTUN, 2006. Transductive Gaussian Process Regression with Automatic Model Selection In: Proceedings of the 17th European Conference on Machine Learning.
  • THOMAS GÄRTNER, QUOC V. LE, SIMON BURTON, ALEX J. SMOLA and SVN. VISHWANATHAN, 2006. Large-Scale Multiclass Transduction In: Advances in Neural Information Processing Systems.
  • KURT DRIESSENS, JAN RAMON and THOMAS GÄRTNER, 2006. Graph Kernels and Gaussian Processes for Relational Reinforcement Learning Machine Learning.
  • THOMAS GÄRTNER, LLOYD, JOHN W. and PETER A. FLACH A., 2004. Kernels and Distances for Structured Data Machine Learning. 57(3),
  • TAMAS HORVATH, THOMAS GÄRTNER and STEFAN WROBEL, 2004. Cyclic Pattern Kernels for Predictive Graph Mining In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • THOMAS GÄRTNER, PETER A. FLACH and WROBEL, STEFAN, 2003. On Graph Kernels: Hardness Results and Efficient Alternatives In: Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop.
  • THOMAS GÄRTNER, 2003. A Survey of Kernels for Structured Data SIGKDD Explorations.
  • THOMAS GÄRTNER, PETER A. FLACH A., KOWALCZYK, ADAM and SMOLA, ALEX J., 2002. Multi-Instance Kernels In: Proceedings of the 19th International Conference on Machine Learning.

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