Matti Kääriäinen

I have moved to NRC Ruoholahti (new homepage). I continue to serve the University of Helsinki as an Adjunct Professor in Computer Science. University email: matti.kaariainen at cs.helsinki.fi.

Sinuhe:

I've developed a machine translation system called Sinuhe. For details, see the paper and the source release below.

Publications:

An analysis of reduced error pruning, Journal of Artificial Intelligence Research 15 (Sept. 2001) 163-187. (With T. Elomaa)
On the practice of branching program boosting. In L. De Raedt & P. Flach (eds.), Machine Learning: ECML 2001, Proc. Twelfth European Conference (pp. 133-144). Lecture Notes in Artificial Intelligence 2167. Springer , 2001. (With T. Elomaa)
The difficulty of reduced error pruning of leveled branching programs, AI&M 7-2002, Seventh International Symposium on Artificial Intelligence and Mathematics (With T. Elomaa)
Extended journal version in Annals of Mathematics and Artificial Intelligence 41 (1), pages 111-124, May 2004.
Progressive Rademacher sampling. Proc. 18th National Conference on Artificial Intelligence, AAAI-2002 (pp. 140-145). AAAI Press & MIT Press, 2002. (With T. Elomaa)
Reduced Error Pruning of Branching Programs Cannot Be Approximated to within a Logarithmic Factor, Information Processing Letters 87, 2 (2003) 73-78. (With R. Nock and T. Elomaa)
Rademacher penalization over decision tree prunings. In N. Lavrac, D. Gamberger, H. Blockeel & L. Todorovski (eds.), Machine Learning: ECML 2003, Proc. 14th European Conf. (pp. 193-204). LNAI 2837. Springer, 2003. With T. Elomaa.
Relating the Rademacher and VC bounds. Department of Computer Science, Series of Publications C, Report C-2004-57, 2004.
Selective Rademacher penalization and reduced error pruning of decision trees. Journal of Machine Learning Research, volume 5: 1107-1126, 2004. With T. Malinen and T. Elomaa.
Generalization error bounds using unlabeled data, In Learning Theory: 18th Annual Conference on Learning Theory, COLT '05 (pp. 127-142).
A comparison of tight generalization error bounds, In The 22nd International Conference on Machine Learning (ICML 2005). With John Langford. For software, look here.
Semi-Supervised Model Selection Based on Cross-Validation, Special Session on Model Selection, IJCNN'06. Previously appeared as a Technical Report TR-05-010 at the International Computer Science Institute (ICSI), 2005.
Active Learning in the Non-realizable Case, to appear at ALT, 2006.

Workshop presentations:

Using Unlabeled Data in Generalization Error Bounds, (Ab)use of Bounds workshop, NIPS 2004, Whistler, Canada.
On active learning in the non-realizable case, oral presentation at the Foundations of Active Learning workshop at NIPS, Dec 2005.
Active learning under arbitrary distributions, poster at the Value of Information in Inference, Learning, and Decision-Making workshop at NIPS, Dec 2005. (Joint work with Claire Monteleoni)
Lower bounds for reductions, oral presentation at the Atomic Learning workshop at TTI-C, March 2006. (Joint work with John Langford)
Learning (and reliability), oral presentation at the Theory of Networked computation workshop, ICSI, Berkeley, March 2006.

Theses:

M.Sc. thesis (in Finnish): Koneoppimismenetelmien yleistysvirheen data- ja algoritmiriippuvainen analyysi (Data and algorithm dependent generalization error analysis of machine learning methods), Department of Computer Science, University of Helsinki, Report C-2002-40, February 2002. (Awarded a Pro Gradu prize by the Faculty of science)
Ph.D. thesis: Learning Small Trees and Graphs that Generalize, Department of Computer Science, University of Helsinki, Report A-2004-7, September 2004.

Other:

Foundations of Active Learning workshop at NIPS 2005.

Department of Computer Science

Department information