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Thomas Gaertner

University of Bonn and Fraunhofer IAIS
Computational Aspects of Mining and Learning
Tel.: +49 2241 14 3513    E-Mail: thomas.gaertner iai.uni-bonn.de
I am more actively maintaining http://www.thomasgaertner.org
Schloss Birlinghoven
53757 Sankt Augustin
 

"Our critics are our friends, because they show us our faults." - Benjamin Franklin
"Even if you're on the right track, you'll get run over if you just sit there" - Will Rogers

 

Overview | Publications | Activities | Research Interests | CV ]

Publications

Some selected publications are listed below and can see the complete list by clicking `show all' above. I am trying to keep the list up to date but it is more likely that my [@scholar/citations] profile is more up to date. You can also look [@DBLP], [@springer], [@scholar], or [@acm]. Now there is also a list [@microsoft academic search].

  • Dino Oglic, Daniel Paurat, . Interactive Knowledge-Based Kernel PCA. Proceedings of ECML PKDD 2014, 2014. Springer. [ Preprint version | BibTeX ]
  • Daniel Paurat, . InVis: A Tool for Interactive Visual Data Analysis. Proceedings of ECML PKDD 2013, 2013. [ draft | BibTeX ]
  • Mario Boley, Sandy Moens, . 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. [ software | slides | BibTeX ]
  • Olana Missura, . 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 | BibTeX ]
  • Mario Boley, Claudio Lucchese, Daniel Paurat, . Direct Local Pattern Sampling by Efficient Two-Step Random Procedures. The 17th annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011. [ BibTeX ]
  • J. Humrich, , 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. [ BibTeX ]
  • Mario Boley, Henrik Grosskreutz, . 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. | BibTeX ]
  • Hanna Geppert, Jens Humrich, Dagmar Stumpfe, , . 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. [ BibTeX ]
  • , 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. [ preprint | BibTeX ]
  • Shankar Vembu, , Mario Boley. Probabilistic Structured Predictors. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI), 2009. [ pdf | BibTeX ]
  • Karina Zapien, , Gilles Gasso, Stephane Canu. Regularization path for ranking svm. Proceedings of the 11th European Symposium on Artificial Neural Networks, April 2008. [ BibTeX ]
  • Hanna Geppert, , , Stefan Wrobel, . 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. [ BibTeX ]
  • Olana Missura, . Online Adaptive Agent for Connect Four. Proceedings of the Fourth International Conference on Games Research and Development CyberGames 2008, page 1-8. 2008. [ pdf | BibTeX ]
  • , Gemma C. Garriga. The Cost of Learning Directed Cuts. Proceedings of the 18th European Conference on Machine Learning, 2007. [ pdf | BibTeX ]
  • Quoc V. Le, Alex J. Smola, , Yasemin Altun. Transductive Gaussian Process Regression with Automatic Model Selection. Proceedings of the 17th European Conference on Machine Learning, 2006. Springer-Verlag. [ @springer | BibTeX ]
  • Thomas Gärtner, Gemma C. Garriga, Thorsten Meinl, editor(s). Proceedings of The Workshop on Mining and Learning with Graphs. 2006. [ pdf | BibTeX ]
  • , Quoc V. Le, Simon Burton, Alex J. Smola, S.V.N. Vishwanathan. Large-Scale Multiclass Transduction. Advances in Neural Information Processing Systems 18, 2006. [ @books.nips | BibTeX ]
  • Ulf Brefeld, , Tobias Scheffer, Stefan Wrobel. Efficient Co-Regularised Least Squares Regression. Proceedings of the 23rd International Conference on Machine Learning, 2006. ACM Press. [ @imls | BibTeX ]
  • Quoc V. Le, Alex J. Smola, . Simpler Knowledge-based Support Vector Machines. Proceedings of the 23rd International Conference on Machine Learning, 2006. ACM Press. [ @imls | BibTeX ]
  • Kurt Driessens, Jan Ramon, . Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. Machine Learning, 2006. [ @springer | BibTeX ]
  • . Kernels for Structured Data. Universität Bonn, 2005. [ buy it from amazon.de, amazon.co.uk, or elsewhere ]. [ BibTeX ]
  • Tamas Horvath, , Stefan Wrobel. Cyclic Pattern Kernels for Predictive Graph Mining. Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2004. [ @portal.acm | BibTeX ]
  • , John W. Lloyd, Peter A. Flach. Kernels and Distances for Structured Data. Machine Learning, 2004. [ @springer | BibTeX ]
  • , Peter A. Flach, 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 | BibTeX ]
  • . A Survey of Kernels for Structured Data. SIGKDD Explorations, 2003. [ @portal.acm | @sigkdd | BibTeX ]
  • , Kurt Driessens, Jan Ramon. Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. Proceedings of the 13th International Conference on Inductive Logic Programming, 2003. [ pdf | BibTeX ]
  • , Peter A. Flach, Adam Kowalczyk, Alex J. Smola. Multi-Instance Kernels. Proceedings of the 19th International Conference on Machine Learning, 2002. Morgan Kaufmann. [ BibTeX ]
  • , John W. Lloyd, Peter A. Flach. Kernels for Structured Data. Proceedings of the 12th International Conference on Inductive Logic Programming, 2002. Springer-Verlag. [ BibTeX ]
  • , Peter A. Flach. WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. Proceedings of the 18th International Conference on Machine Learning, 2001. Morgan Kaufmann. [ BibTeX ]
Overview | Publications | Activities | Research Interests | CV ]

Activities

  • Action Editor since 2009 and Editorial Board Member since 2006 of the Machine Learning Journal ( MLJ )
  • PhD Committees Koen Smets (University of Antwerp), Martial Hue (Ecole des Mines de Paris), Wannes Meert (KU Leuven), Licentiate discussion leaer Frederik Johansson (Chalmer University of Technology)
  • Funding
    • I received an Emmy Noether grant in 2010 for Machine Learning Algorithms for Constructing Novel Relational Structures
    • My project on Effective Well-Behaved Pattern Mining through Sampling is funded by a research grant from the DFG
  • Organisation
    • member of the technical organisation committee of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, which will be held in Riva del Garda in 2016.
    • co-chair of the first worksop on Constructive Machine Learning at NIPS 2013
    • co-chair of the first worksop on Mining and Learning with Graphs at ECMLPKDD 2016
    • co-chair of the project workshop on Learning and Inference
  • Tutorials, Colloquia, Invited Talks, ...
    • In 2014 I gave invited talks at the ECML workshop on New Frontiers in Mining Complex Patterns, at Microsoft Research in Remond, at the Tokyo Institute of Technology, and at Chalmers University of Technology. Thanks to my hosts!
    • Algorithms for Predicting Structured Data tutorial with Shankar Vembu at the European Conference on Machine Learning ( more details )
    • Kernels for Structured Data tutorial at the International Conference on Machine Learning  ( more details ).
    • Kernel-Based Learning from Structured Data at the 2002 Summer School on Relational Data Mining in Helsinki. [handouts]
  • Recent Programm Committee Memberships
    • International Conference on Machine Learning ( regularly PC member or Area Chair )
    • Conference on Artificial Intelligence ( Senior PC member AAAI-2010 )
    • International Joint Conference on Artificial Intelligence ( IJCAI-2009 | Senior PC member IJCAI-2011 )
    • European Conference on Machine Learning ( regularly Area Chair or PC member, regularly journal track guest editorial board member )
    • International Word Wide Web Conference ( WWW-2010 )
    • International Conference on Knowledge Discovery and Data Mining ( regularly PC member )
    • International Workshop on Mining and Learning with Graphs ( regularly PC member | Workshop Co-Chair MLG-2006 | Steering Committee MLG-2007 | Steering Committee MLG-2008 )
  • Guest Editor Machine Learning special issue on Mining and Learning with Graphs
  • Research Proposal Evaluation
    • German Science Foundation ( DFG )
    • The Israel Science Foundation ( ISF ),
    • Research Foundation Flanders ( FWO )
Overview | Publications | Activities | Research Interests | CV ]

Research Interests

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.

 

Overview | Publications | Activities | Research Interests | CV ]

CV (Curriculum Vitae)

I earned a PhD from the University of Bonn (summa cum laude), a MSc from the University of Bristol (with commendation), and a Diplom as well as a degree as a certified engineering assistant from the University of Cooperative Education in Mannheim. During the course of my studies at the University of Bristol, I got interested in machine learning research. Since then, I have created a track record of publications at the highest ranking machine learning and data mining conferences. I investigate theoretical and algorithmical challenges of machine learning and data mining. I am always interested in applications of these algorithms and have for instance worked on chemoinformatics, computer games, sports analytics, and financial time series. Internationally, I am best known for my work on kernels for structured data. In 2010, I received an Emmy Noether grant from the German Science Foundation. I regularly supervise students of all levels, organise seminars and labs, teach courses on kernel methods, and am give tutorials as well as invited talks. I am an action editor of the `Machine Learning' journal and member of the technical organisation committee of the `European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases', which will be held in Riva del Garda in 2016.

 

Overview | Publications | Activities | Research Interests | CV ]

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