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Jesse Alama, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban, and Tom Heskes.
Premise selection for mathematics by corpus analysis and kernel methods.
Journal of Automated Reasoning, 2012. Conditionally accepted. [ bib | http ]
Lennart Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd M.D. Dijkstra, Tom Heskes, and Matthijs Verhage.
Molecular machines in the synapse: Overlapping protein sets control distinct steps in neurosecretion.
PLoS Computation Biology, 2012. Minor revisions pending. [ bib ]
Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, Tapio Salakoski, and Bernard De Baets.
Learning intransitive reciprocal relations with kernel methods.
European Journal of Operational Research, 206(3):676-685, 2010. [ bib | http ]
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, and Tom Heskes.
Co-regularized least-squares for label ranking.
In Eyke Hüllermeier and Johannes Fürnkranz, editors, Preference Learning, pages 107-123, 2010. [ bib | http ]
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski.
Kernels for text analysis.
Advances of Computational Intelligence, 116:81-97, May 2008. [ bib | http ]
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski.
Locality kernels for sequential data and their applications to parse ranking.
Applied Intelligence, 31(1):81-88, 2009. [ bib | http ]
Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jouni Järvinen, and Jorma Boberg.
An efficient algorithm for learning to rank from preference graphs.
Machine Learning, 75(1):129-165, 2009. [ bib | http ]
Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, and Tom Heskes.
Multi-output ranking for automated reasoning.
In International Conference on Knowledge Discovery and Information Retrieval, 2011. [ bib | .pdf ]
Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban, and Tom Heskes.
Learning semantics for automated reasoning.
In Antoine Bordes, Jason Weston, Ronan Collobert, and Leon Bottou, editors, NIPS Workshop on Learning Semantics, 2011. [ bib | .pdf ]
Evgeni Tsivtsivadze, Josef Urban, Herman Geuvers, and Tom Heskes.
Semantic graph kernels for automated reasoning.
In SIAM International Conference on Data Mining, 2011. [ bib | .pdf ]
Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, and Tom Heskes.
Learning2reason.
In James H. Davenport, William M. Farmer, Josef Urban, and Florian Rabe, editors, Calculemus/MKM, volume 6824 of Lecture Notes in Computer Science, pages 298-300, 2011. [ bib | http ]
Evgeni Tsivtsivadze and Tom Heskes.
Sparse preference learning.
NIPS Workshop on Practical Application of Sparse Modeling: Open Issues and New Directions, 2010. [ bib | .pdf ]
Tjeerd Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, and Tom Heskes, editors. Pattern Recognition in Bioinformatics - 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings, volume 6282 of Lecture Notes in Computer Science. Springer, 2010. [ bib | http ]
Antolin Janssen, Evgeni Tsivtsivadze, Jorma Boberg, Tjeerd Dijkstra, and Tom Heskes.
Efficient remote homology detection.
Pattern Recognition in Bioinformatics, 2010. [ bib | .pdf ]
Evgeni Tsivtsivadze, Botond Cseke, and Tom Heskes.
Kernel principal component ranking: Robust ranking on noisy data.
ECML/PKDD-Workshop on Preference Learning (PL-09), 2009. [ bib | .pdf ]
Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd Dijkstra, Tom Heskes, and Matthijs Verhage.
Identification of presynaptic gene clusters in synaptic signaling using functional data from genetic perturbation studies in hippocampal autapses.
2nd INCF Congress of Neuroinformatics, Frontiers in Neuroinformatics, 2009. [ bib | http ]
Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, Tapio Salakoski, and Bernard De Baets.
From ranking to intransitive preference learning: Rock-paper-scissors and beyond.
ECML/PKDD-Workshop on Preference Learning (PL-09), 2009. [ bib | .pdf ]
Evgeni Tsivtsivadze, Fabian Gieseke, Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski.
Leanring preferences with co-regularized least squares.
ECML/PKDD-Workshop on Preference Learning (PL-08), pages 55-62, 2008. [ bib | .pdf ]
Evgeni Tsivtsivadze, Tapio Pahikkala, Antti Airola, Jorma Boberg, and Tapio Salakoski.
A sparse regularized least-squares preference learning algorithm.
Scandinavian Conference on Artificial Intelligence (SCAI 2008), pages 76-83. IOS Press, 2008. [ bib | .pdf ]
Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jorma Boberg, and Tapio Salakoski.
Regularized least-squares for learning non-transitive preferences between strategies.
Finnish Artificial Intelligence Conference , pages 94-98, IOS Press, 2008. [ bib ]
Evgeni Tsivtsivadze, Jorma Boberg, and Tapio Salakoski.
Locality kernels for protein classification.
International Workshop on Algorithms in Bioinformatics, (WABI 2007), pages 2-11. Springer, 2007. [ bib ]
Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jorma Boberg, and Tapio Salakoski.
Learning to rank with pairwise regularized least-squares.
SIGIR Workshop on Learning to Rank for Information Retrieval, pages 27-33, 2007. [ bib | .pdf ]
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski.
Locality-convolution kernel and its application to dependency parse ranking.
IEA/AIE 2006, Annecy, France, volume 4031 of Lecture Notes in Computer Science, pages 610-618. Springer, 2006. [ bib | .pdf ]
Tapio Pahikkala, Evgeni Tsivtsivadze, Jorma Boberg, and Tapio Salakoski.
Graph kernels versus graph representations: a case study in parse ranking.
ECML/PKDD'06 workshop on Mining and Learning with Graphs (MLG 2006), Berlin, Germany, 2006. [ bib | .pdf ]
Evgeni Tsivtsivadze, Tapio Pahikkala, Sampo Pyysalo, Jorma Boberg, Aleksandr Mylläri, and Tapio Salakoski.
Regularized least-squares for parse ranking.
Advances in Intelligent Data Analysis VI (IDA 2005), pages 464-474. Springer, 2005. [ bib | .pdf ]
Filip Ginter, Tapio Pahikkala, Sampo Pyysalo, Evgeni Tsivtsivadze, Jorma Boberg, Jouni Jarvinen, Aleksandr Myllari, and Tapio Salakoski.
Information extraction from biomedical text: The biotext project.
Baltic Conference on Human Language Technologies (HLT 2005), pages 131-136. 2005. [ bib | .pdf ]

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