Selected publications

Conference Proceedings

• Schneider, J. C.,
  Unambiguous Erasing Morphisms in Free Monoids. In: Mogens Nielsen, Antonin Kucera, Peter Bro Miltersen, Catuscia Palamidessi, Petr Tuma, Frank Valencia (Editors),
  Proceedings of SOFSEM 2009: Theory and Practice of Computer Science, LNCS 5404, pages 473-484, Springer-Verlag, 2009.
  Best Student SOFSEM'09 Paper Award
  
  
• Schneider, J. C.,
  Eindeutige löschende Homomorphismen in freien Monoiden. In: Markus Holzer, Martin Kutrib, Andreas Malcher (Herausgeber), Tagungsband des 18. Theorietags der GI Fachgruppe 0.1.5, Automaten und Formale Sprachen, 2008, Universität Gießen, 115-119.
  
  
• Reidenbach, D., Schneider, J.C.,
  Ein alternativer Primitivitätsbegriff für Wörter. Tagungsband zum 17. Theorietag der GI Fachgruppe 0.1.5, Automaten und Formale Sprachen, 2007, Universität Leipzig, 116-120.

• Reidenbach, D., Schneider, J.C.,
  Morphically Primitive Words. Proceedings of the 6th International Conference on Words, WORDS 2007, 262-272.
  

• Freydenberger, D.D., Reidenbach, D.,
  The Unambiguity of Segmented Morphisms. Proceedings of the 11th International Conference on Developments in Language Theory, DLT 2007, Lecture Notes in Computer Science 4588, 181-192.

• Freydenberger, D.D., Reidenbach, D., Schneider, J.C.,
  Unambiguous Morphic Images of Strings. Proceedings of the 9th International Conference on Developments in Language Theory, DLT 2005, Lecture Notes in Computer Science 3572, 248-259.
  

• Reidenbach, D.,
  Inductive Inference of E-pattern Languages: A Progress Report. Proceedings of the Workshop on Theoretical Aspects of Grammar Induction, TAGI 2005, Technical Report WSI-2005-14, Universität Tübingen, 30-33.
  

• Freydenberger, D.D., Reidenbach, D., Schneider, J.C.,
  Eindeutige Homomorphismen in freien Monoiden. Tagungsband zum 15. Theorietag der GI Fachgruppe 0.1.5, Automaten und Formale Sprachen, 2005, Technical Report WSI-2005-16, Universität Tübingen, 18-21.
  

• Lange, S., Zilles, S.,
  Comparison of query learning and Gold-style learning in dependence of the hypothesis space. Proceedings of the 15th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 3244, 99-113, Springer-Verlag 2004.
• 
  
• Lange, S., Zilles, S.,
  Replacing limit learners with equally powerful one-shot query learners. Proceedings of the 17th Annual Conference on Learning Theory, Lecture Notes in Artificial Intelligence 3120, 155-169, Springer-Verlag 2004.

• Reidenbach, D.,
  On the Learnability of E-pattern Languages over Small Alphabets. Proceedings of the 17th Conference on Learning Theory, COLT 2004, Lecture Notes in Artificial Intelligence 3120 (2004), 140-154.
  M. Fulk Award for COLT'04.
  

• Reidenbach, D.,
  On the equivalence problem for E-pattern languages over small alphabets. Proceedings of the 8th International Conference on Developments in Language Theory, Lecture Notes in Computer Science 3340 (2004) 368-380.
  Best Student DLT’04 Paper Prize

• Reidenbach, D., A Discontinuity in Pattern Inference. Proceedings of the 21st International Symposium on Theoretical Aspects of Computer Science, STACS 2004, Lecture Notes in Computer Science 2996 (2004), 129-140.
  

• Jain, S., Kinber, E., Wiehagen, R.,
  Learnig all subfunctions of a function. Proceedings of the 16th Annual Conference on Learning Theory and 7th Annual Workshop on Kernel Machines, B. Schölkopf and M. K. Warmuth, Eds., Lecture Notes in Artificial Intelligence 2777 (2003) 714-728.
  

• Lange, S., Zilles, S.,
  On the learnability of erasing pattern languages in the query model. Proceedings of the 14th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 2842 (2003) 129-143.

• Zilles, S.,
  Intrinsic complexity of uniform learning. Proceedings of the 14th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 2842 (2003) 39-53.
  

• Lange, S., Zilles, S.,
  Formal models of incremental learning and their analysis. Proceedings of the International Joint Conference on Neural Networks, 2691-2696, IEEE Press (2003).

• Grieser, G., Lange, S. und Memmel, M.,
  DaMiT: Ein adaptives Tutorsystem für Data-Mining. Tagungsband Leipziger Informatik-Tage 2003, 192-203, infix, Akademische Verlagsgesellschaft.

• Memmel, M.,
  Bausteine eines durchgängigen Workflows für die Inhaltserstellung in e-Learning-Systemen. Tagungsband Leipziger Informatik-Tage 2003, 133-142, infix, Akademische Verlagsgesellschaft.

• Lange, S., Zilles, S.,
  Data Mining für die Klassifikation von Transmembran-Proteinen. Tagungsband 4. Informatiktag der Hochschule Anhalt 2003.

• Reidenbach, D.,
  A negative result on inductive inference of extended pattern languages. Proceedings of the 13th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 2533 (2002) 308-320.
  E.M.GOLD Award for ALT'02.

• Zilles, S.,
  Merging uniform inductive learners. Proceedings 15th Annual Conference on Computational Learning Theory, Lecture Notes in Artificial Intelligence 2375 (2002) 201-215.
  COLT'02 Best Student Author Prize.

• Case, J., Jain, S., Stephan, F., Wiehagen, R.,
  Robust learning - rich and poor. Proceedings 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory, Lecture Notes in Artificial Intelligence 2111 (2001) 143-159.

• Jain, S., Kinber, E., Wiehagen, R., Zeugmann, T,
  Learning recursive functions refutably. Proceedings 12th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 2225 (2001) 283-298.
• 
  
• Zilles, S.,
  On the synthesis of strategies identifying recursive functions. Proceedings 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory, Lecture Notes in Artificial Intelligence 2111 (2001) 160-176.

• Zilles, S.,
  On the comparison of inductive inference criteria for uniform learning of finite classes. Proceedings 12th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 2225 (2001) 251-266.

• Jain, S., Kinber, E., Wiehagen, R.,
  Language learning from texts: Degrees of intrinsic complexity and their characterizations. Proceedings 13th Annual Conference on Computational Learning Theory, pp.47-58, Morgan Kaufmann 2000.

• Nessel, J., Lange, S.,
  Learning erasing pattern languages with queries. Proceedings 11th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1968 (2000) 86-100.

• Kinber, E., Papazian, Chr., Smith, C., Wiehagen, R.,
  On the intrinsic complexity of learning recursive functions. Proceedings 12th Annual Conference on Computational Learning Theory, COLT'99, pp.257-266, ACM Press, 1999.

• Nessel, J.,
  Learnability of enumerable classes of recursive functions from "typical" examples. Proceedings 10th International Conference of Algorithmic Learning Theory, ALT'99, Lecture Notes in Artificial Intelligence 1720 (1999) 264-275.

• Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, Th. (Eds.),
  Algorithmic Learning Theory. Proceedings 9th International Conference ALT'98, Lecture Notes in Artificial Intelligence, Vol. 1501, 439 pp., Springer 1998.

• Jain, S., Smith, C., Wiehagen, R.,
  On the power of learning robustly. Proceedings 11th Annual Conference on Computational Learning Theory, 187-197, ACM Press 1998.

• Nessel, J.,
  Birds can fly... Proceedings 11th Annual Conference on Computational Learning Theory, 56-63, ACM Press 1998.

• Stein, W.,
  Consistent polynomial identification in the limit. Proceedings 9th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1501 (1998) 424-438.

• Freivalds, R., Tervits, G., Wiehagen, R., Smith, C.,
  Learning small programs with additional information. Proceedings 4th Symposium on Logical Foundations of Computer Science, Lecture Notes in Computer Science 1234 (1997) 102-112.

• Jain, S., Lange, S., Nessel, J.,
  Learning of r.e. languages from good examples. Proceedings 8th International Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1316 (1997) 32-47.

• Jain, S., Kinber, E., Wiehagen, R.,
  On learning and co-learning of minimal programs. Proceedings 7th International Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1160 (1996) 242-255.

• Lange, S., Wiehagen, R., Zeugmann, Th.,
  Learning by erasing. Proceedings 7th International Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 1160 (1996) 228-241.

• Stein, W.,
  Schnelles konsistentes Lernen. Tagungsband 4. Leipziger Informatik-Tage 1996, 197-202.

• Stein, W.,
  Learning enviroments leading to inefficient learnabilitiy. Proceedings 27. Workshop über Komplexitätstheorie, Datenstrukturen und Effiziente Algorithmen, TU München, TUM-INFO-11-1195-I 9530, November 1995.

• Freivalds, R., Botuscharov, O., Wiehagen, R.,
  Identifying nearly minimal Gödel numbers from additional information. Proceedings 4th International Workshop on Analogical and Inductive Inference, Lecture Notes in Artificial Intelligence 872 (1994) 91-99.

• Jantke, K.P., Wiehagen, R., Lange, S.,
  GOSLER - Algorithmisches Lernen für wissensbasierte Systeme. Proceedings Statusseminar "Künstliche Intelligenz" des BMFT, Wolf, G., Ed., Deutsche Forschungsanstalt für Luft- und Raumfahrt (1994) 269-298.

• Lange, S., Nessel, J., Wiehagen, R.,
  Language learning from good examples. Proceedings 5th International Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 872 (1994) 423-437.

• Stein, W.,
  Against all odds. Proceedings 22. Workshop über Komplexitätstheorie, Datenstrukturen und Effiziente Algorithmen, MPI-I-94-104 (1994).

• Stein, W.,
  Exploring the sources of computational inefficiency in inductive inference. Proceedings 23. Workshop über Komplexitätstheorie, Datenstrukturen und Effiziente Algorithmen, tr-ri-94-147, Univ.-GH Paderborn (1994).

• Wiehagen, R., Smith, C.H., Zeugmann, Th.,
  Classification of predicates and languages. Computational Learning Theory: EuroCOLT'93, J. Shawe-Taylor, M. Anthony, Eds., pp.171-181, Clarendon Press 1994.

• Wiehagen, R.,
  From inductive inference to algorithmic learning theory. Proceedings, Third Workshop on Algorithmic Learning Theory, Tokyo, Lecture Notes in Artificial Intelligence 743 (1993) 13-24.

• Freivalds, R., Kinber, E.B., Wiehagen, R.,
  Dual types of hypotheses in inductive inference. Proceedings, Second Workshop on Nonmonotonic and Inductive Logic, Karlsruhe, Lecture Notes in Artificial Intelligence 659 (1993) 209-240.

• Kinber, E., Smith, C.H., Velauthapillai, M., Wiehagen, R.,
  On learning multiple concepts in parallel. Proceedings, Sixth Annual ACM Conference on Computational Learning Theory, 175-181, ACM Press 1993.

• Wiehagen, R., Smith, C.H.,
  Generalization versus classification. Proceedings Fifth Annual ACM Workshop on Computational Learning Theory, ACM Press (1992) 224-230.

• Wiehagen, R.,
  A thesis in inductive inference. Proceedings International Workshop on Nonmonotonic and Inductive Logic, Dix, J., Jantke, K.P., Schmitt, P.H., Eds., Lecture Notes in Artificial Intelligence 543 (1991) 184-207.