publications
2007
 K. Kersting, B. Milch, L.S. Zettlemoyer, M. Haimes, L. Pack Kaelbling. Reasoning about Large Populations with Lifted Probabilistic Inference. 2page abstract in L. Getoor, R. Gottardo, K. Murphy, E. Xing, editors, Working Notes of the NIPS07 Workshop on Statistical Model of Networks, December 8th, 2007, Whistler, BC, Canada, 2007.
 I. Thon, K. Kersting. Distributional Relational State Representations for Complex Stochastic Processes. To appear in D. Malerba, A. Appice, and M. Ceci, editors, Working Notes of the 6th Workshop on MultiRelational Data Mining (MRDM07) at ECML/PKDD07, Warsaw, Poland, Sept. 17, 2007.
[draft]
An extended abstract appeared in P. Frasconi, K. Kersting, K. Tsuda, editors, Working Notes of the 5th International Workshop on Mining and Learning with Graphs (MLG), Universita degli Studi di Firenze, Florence,Aug 13, 2007.  B. Gutmann, K. Kersting. Stratified Gradient Boosting for Fast Training CRFs. To appear in D. Malerba, A. Appice, and M. Ceci, editors, Working Notes of the 6th Workshop on MultiRelational Data Mining (MRDM07) at ECML/PKDD07, Warsaw, Poland, Sept. 17, 2007.
An extended Abstract to appear in P. Frasconi, K. Kersting, K. Tsuda, editors, Working Notes of the 5th International Workshop on Mining and Learning with Graphs (MLG), Universita degli Studi di Firenze, Florence,Aug 13, 2007.  S. Sanner, K. Kersting. Symbolic Dynamic Programming. Chapter to appear in C. Sammut, editor, Encyclopedia of Machine Learning, SpringerVerlag, 2007.
[draft]  S. Ganzert, K. Moeller, K. Kersting, L. De Raedt, J. Guttmann. Equation Discovery for Model Identification in Respiratory Mechanics under Conditions of Mechanical Ventilation.
In W. Bridewell, L. Todorovski, S. Kramer, working notes of
the 1st International Workshop on the Induction of Process Modells
(IPM07) at ICML07, Corvallis, OR, USA, June 24, 2007.
[draft]  K. Kersting, C. Plagemann, A. Cocora, W. Burgard, L. De Raedt. Learning to Transfer Optimal Navigation Policies. To appear in Advanced Robotics. Special Issue on Imitative Robots, 21(9), September 2007.
 K. Kersting, C. Plagemann, P. Pfaff, W. Burgard. MostLikely Heteroscedastic Gaussian Process Regression.
In the Proceedings of the 24th Annual International
Conference on Machine Learning (ICML07), Corvallis, OR, USA, June
2024, 2007.
[draft]  C. Plagemann, K. Kersting, P. Pfaff, W. Burgard. Gaussian Beam Processes: A Nonparametric
Bayesian Measurement Model for Range Finders. In the Proceedings of the Robotics: Science and Systems Conference (RSS07), Atlanta, GA, USA, June 2730, 2007.
[draft]
An extenden abstract appeared as Heteroscedastic Gaussian Process Regression for Modeling Range Sensors in Mobile Robotics on invitation in the Proceedings of the Learning Workshop (Snowbird), Puerto Rico, March 1922, 2007.  N. Landwehr, K. Kersting, L. De Raedt. nFOIL: Integrating Naive Bayes and FOIL. Journal of Machine Learning Research (JMLR) 8(Mar):481507, 2007.
[draft] [software]  L. De Raedt, K. Kersting, A. Kimmig, K. Revoredo, H. Toivonen. Revising Probabilistic Prolog Programs.
Accepted for publication in the Machine Learning Journal (MLJ),
ILP2006 Special Issue, S. H. Muggleton, R. Otero, Simon Colton, guest
editors. [draft]
An extended abstract appeared in the Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming (ILP06), Santiago, Spain, August 2427, 2006.
2006
 A. Karwarth, K. Kersting. Relational Sequence Alignments and Logos.
In S. H. Muggleton and R. Otero, editors, Proceedings of of the 16th
International Conference on Inductive Logic Programming (ILP06),
Santiago, Spain, August 2427, 2006.
[draft] [REAListic web server]
An earlier exended abstract appeared as On Relational Sequence Alignments and Their Information Contents in S. H. Muggleton and R. Otero, editors, Short Paper Preceedings of the 16th International Conference on Inductive Logic Programming (ILP06), Santiago, Spain, August 2427, 2006.
A short paper also appeared as Relational Sequence Alignment in T. Gaertner, G. C. Garriga, T. Meinl, editors, Working Notes of the ECML06 Workshop on Mining and Learning with Graphs (MLG 2006), Berlin, Germany, September 18th, 2006.[draft]  T. Guerel, K. Kersting, S. Kandler, U. Egert, S. Rotter, L. De Raedt. Learning the functional connectivity in neuronal cultures. Poster Presentations at the 2nd Bernstein Symposium, Berlin, Germany, Oct. 2006.
 K. Kersting. B. Gutmann. Unbiased Conjugate Direction Boosting for Conditional Random Fields. Short paper in T. Gaertner, G. C. Garriga, T. Meinl, editors, Working Notes of the ECML06 Workshop
on Mining and Learning with Graphs (MLG 2006), Berlin, Germany, September 18th, 2006.
[draft]  B. Gutmann, K. Kersting. TildeCRF: Conditional Random Fields for Logical Sequences.
In J. Fuernkranz, T. Scheffer, M. Spiliopoulou, editors, Proceedings of
the 17th European Conference on Machine Learning (ECML2006), pages
174185, Berlin, Germany, September 1822, 2006.
[draft] [software] Best student paper award  U. Dick K. Kersting. Fisher Kernels for Relational Data.
In J. Fuernkranz, T. Scheffer, M. Spiliopoulou, editors, Proceedings of
the 17th European Conference on Machine Learning (ECML2006),pages
114125, Berlin, Germany, September 1822, 2006.
[draft]  A. Cocura, K. Kersting, C. Plageman, W. Burgard, L. De Raedt. Learning Relational Navigation Policies. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS06), Bejing, China, October 915, 2006
[draft]
A slightly different version appeared in H.M. Gross, editor, Special Issue "Lernen und Selbstorganisation von Verhalten", Kuenstliche Intelligenz (3/06):1218.
 M. Jaeger, K. Kersting, L. De Raedt. Expressivity Analysis for PLLanguages.
In A. Fern, L. Getoor, and B. Milch, editors, Working Notes of the
ICML06 Workshop "Open Problems in Statistical Relational Learning"
(SRL06), Pittsburgh, USA, June 29, 2006.
[draft]  R. Triebel. K. Kersting. W. Burgard. Robust 3D Scan Point Classification using Associative Markov Networks. In N. Papanikolopoulos, editor, IEEE International Conference on
Robotics and Automation (ICRA06), Walt Disney World Resort in Orlando, Florida, USA, May 1519, 2006.
[draft]  K.Kersting, L. De Raedt, T. Raiko. Logical Hidden Markov Models. Journal of Artificial Intelligence Research, Volume 25, pages 425456, 2006.
[draft] 
K. Kersting. An Inductive Logic Programming Approach to Statistical Relational Learning. Frontiers in Artificial Intelligence and its Applications series (Dissertations), Volume 148, IOS Press, Amsterdam, The Netherlands, 2006. ISBN 1586036742, LCCN 2006932504. [@IOS Press]
This is a reprint of my Ph.D Thesis, Institute for Computer Science, AlbertLudwigs University of Freiburg, Germany, April 2006, for which I have received the ECCAI 2006 Artificial Intelligence Dissertation Award .
An extended abstract appeared as: K. Kersting. An Inductive Logic Programming Approach to Statistical Relational Learning. AI Communications 19(4):389390, 2006.
2005
 K. Kersting, L. De Raedt. Bayesian Logic Programming: Theory and Tool. Chapter to appear in L. Getoor and B. Taskar, editors, An Introduction to Statistical Relational Learning, MIT Press.
[draft] [software]  T. Guerel, K. Kersting. On the TradeOff Between Iterative Classification and Collective Classification: First Experimental Results.
In S. Nijssen, T. Meinl, and G. Karypis, editors, Working Notes of the
Third International ECML/PKDD Workshop on Mining Graphs, Trees and
Sequences (MGTS05), Porto, Portugal, October 7, 2005.
[draft]  K.Kersting, T. Raiko. 'Say EM' for Selecting Probabilistic Models for Logical Sequences.
In F. Bacchus and T. Jaakkola, editors, Proceedings of the 21st
Conference on Uncertainty in Artificial Intelligence (UAI05),
Edinburgh, Scotland, July 2629, 2005.
[draft]  N. Landwehr, K. Kersting, L. De Raedt. nFOIL: Integrating Naive Bayes and FOIL.
In M. Veloso and S. Kambhampati, editors, Proceedings of the Twentieth
National Conference on Artificial Intelligence (AAAI05), pages
795800, Pittsburgh, Pennsylvania, USA, July 913, 2005.
[draft]  L. De Raedt, K. Kersting, S. Torge. Towards Learning Stochastic Logic Programs from ProofBanks.
In M. Veloso and S. Kambhampati, editors, Proceedings of the Twentieth
National Conference on Artificial Intelligence (AAAI05), pages
752757, Pittsburgh, Pennsylvania, USA, July 913, 2005.
[draft]
An earlier version appeared as: K. Kersting, T. Raiko, L. De Raedt. A Structural GEM for Learning Logical Hidden Markov Models. In S. Dzeroski, L. De Raedt, and S. Wrobel, editors, Working Notes of the Second KDDWorkshop on MultiRelational Data Mining (MRDM03), Washington, DC, USA, August 27, 2003.
2004
 L. De Raedt, K. Kersting. Probabilistic Inductive Logic Programming.
Invited paper in S. BenDavid, J. Case and A. Maruoka, editors,
Proceedings of the 15th International Conference on Algorithmic
Learning Theory (ALT2004), pages 1936. Padova, Italy, October 25,
2004.
[draft]  K. Kersting, U. Dick. Balios  The Engine for Bayesian Logic Programs.
Demonstration paper in J.F. Boulicaut, F. Esposito, F. Giannotti and
D. Pedreschi, editors, Proceedings of the 8th European Conference on
Principles and Practice of Knowledege Discovery in Databases
(PKDD2004), pages 549551. Pisa, Italy, September 2025, 2004.
[draft] [software]  K. Kersting, T. Gaertner. Fisher Kernels for Logical Sequences.
In J.F. Boulicaut, F. Esposito, F. Giannotti and D. Pedreschi,
editors, Proceedings of the 15th European Conference on Machine
Learning (ECML2004), pages 205  216. Pisa, Italy, September 2025,
2004.
[draft]
An early draft entitled Fisher Kernels and Logical Sequences with an Application to Protein Fold Recognition was presented at the NIPS 2002 workshop on Machine Learning Techniques for Bioinformatics organized by C. Campbell, F. d'AlcheBuc, P. Long. December (Friday) 13, 2002, Vancouver, Canada.[twopage draft]  K. Kersting, L. De Raedt. Logical Markov Decision Programs and the Convergence of Logical TD(λ).
In A. Srinivasan, R. King, and R.Camacho, editors, Proceedings
of the Fourteenth International Conference on Inductive Logic
Programming (ILP2004), pages 180197. Porto, Portugal, September 68,
2004.
[draft]
An earlier version introducing LOMDPs and showing some experiments with "logical Qlearning" appeared as Logical Markov Decision Programs in L. Getoor and D. Jensen, editors, Working Notes of the IJCAI2003 Workshop on Learning Statistical Models from Relational Data (SRL03), pp. 6370, August 11, Acapulco, Mexico, 2003.[draft]  K. Kersting, M. Van Otterlo, L. De Raedt. Bellman goes Relational.
In R. Greiner and D. Schuurmans, editors, Proceedings of the
TwentyFirst International Conference on Machine Learning (ICML2004),
pages 465  472. Banff, Alberta, Canada, July 48, 2004.
[draft]
A twopage abstract will also appear in L. Schomaker, N. Taatgen, R. Verbruggethe, editors, Proceedings of the Sixtheenth BelgianDutch Conference on Artificial Intelligence (BNAIC04), Groning, The Netherlands, October 2122, 2004.  M. Van Otterlo, K. Kersting. Challenges for Relational Reinforcement Learning.
In the Working Notes of the ICML2004 Workshop on Relational
Reinforcement Learning. P. Tadepalli, R. Givan, K. Driessens, editors.
Banff, Alberta, Canada, July 8, 2004.
[draft]  K. Kersting, N. Landwehr. Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm.
Chapter (pp. 235254) in "Advances in Bayesian Networks", Series:
Studies in Fuzziness and Soft Computing, Vol. 146, J. A. Gamez, S.
Moral and A. Salmeron, editors, Springer, 2004.
[draft]
A previous version appeared in J. A. Gamez and A. Salmeron, editors, Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM02), pp. 8998, November 68, 2002, Cuenca, Spain.[draft]
2003
 L. De Raedt, K. Kersting. Probabilistic Logic Learning.
In ACMSIGKDD Explorations, special issue on MultiRelational Data
Mining, S. Dzeroski and L. De Raedt, editors, Vol. 5(1), pp. 3148,
July 2003.
[draft] 
J. Fischer, K. Kersting. Scaled CGEM: A Fast Accelerated EM.
In N. Lavrac, D. Gamberger, H. Blockeel, and L. Todorovski, editors,
Proceedings of the Fourteenth European Conference on Machine Learning
(ECML2003), pp. 133144, Cavtat, Croatia, September 2226, 2003.
[draft]  K. Kersting. Representational power of probabilisticlogical models: From upgrading to downgrading.
In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI2003
Workshop on Learning Statistical Models from Relational Data (SRL03),
pp. 6162, August 11, Acapulco, Mexico, 2003.
[draft]  K. Kersting, T. Raiko, S. Kramer, L. De Raedt. Towards Discovering Structural Signatures of Protein Folds based on Logical Hidden Markov Models.
In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung and T. E. Klein,
editors, Proceedings of the Pacific Symposium on Biocomputing
(PSB2003), pp. 192203, January 37 2003, Kauai, Hawaii, USA.
[draft]
Extended abstract in Stan Matwin and Claude Sammut, editors, WorkinProgress Reports of the Twelfth International Conference on Inductive Logic Programming (ILP 2002),Sydney, Australia, July 911, 2002.[draft]
Technical Report No. 175, Institute for Computer Science, University of Freiburg, Germany, June 2002.[.ps.gz]
2002
 T. Raiko, K. Kersting, J. Karhunen, L. De Raedt. Bayesian Learning of Logical Hidden Markov Models. In Proceedings of the Finnish AI conference (STeP2002), pp. 6471, 1517 December 2002, Oulu, Finland.
[draft]  K. Kersting, T. Raiko, L. De Raedt. Logical Hidden Markov Models (Extended Abstract).
In J. A. Gamez and A. Salmeron, editors, Proceedings of the First
European Workshop on Probabilistic Graphical Models (PGM02), pp.
99107, November 68, 2002, Cuenca, Spain.
[draft]  K. Kersting, L. De Raedt. Basic Principles of Learning Bayesian Logic Programs. Technical Report
No. 174, Institute for Computer Science, University of Freiburg, Germany, June 2002.
[.ps.gz]  S. Ganzert, J. Guttmann, K. Kersting, R. Kuhlen, C. Putensen, M. Sydow, S. Kramer. Analysis of Respiratory PressureVolume Curves in Intensive Care Medicine Using
Inductive Machine Learning.
Artificial Intelligence in Medicine, special issue on Medical Data
Mining, K. Cios, J. Berman, W. Moore, editors, 26(12), pp. 6986,
Sept. 2002.
[draft]
2001

K. Kersting, L. De Raedt. Towards Combining Inductive Logic Programming and Bayesian Networks.
In C. Rouveirol, M. Sebag, editors, Proceedings
of the Eleventh International Conference on Inductive Logic Programming
(ILP2001),pages 118131, LNAI 2157, Springer, Strasbourg, France,
September 2001.
[draft] 
K. Kersting, L. De Raedt. Adaptive Bayesian Logic Programs.
In C. Rouveirol, M. Sebag, editors, Proceedings
of the Eleventh International Conference on Inductive Logic Programming
(ILP2001), pages 104  117, LNAI 2157, Springer, Strasbourg, France,
September 2001.
[draft] 
K. Kersting, L. De Raedt. Bayesian Logic Programs. Technical Report
No. 151, Institute for Computer Science, University of Freiburg, Germany, April 2001.
[.ps.gz]
2000

K. Kersting, L. De Raedt, S. Kramer. Interpreting Bayesian Logic Programs.
In L. Getoor and D. Jensen, editors, Proceedings of the AAAI2000
Workshop on Learning Statistical Models from Relational Data, Technical
Report WS0006, AAAI Press, Austin/Texas, USA, 2000.
[draft]
Previous draft mainly differ in stressing different aspects of Bayesian logic programs: K. Kersting, L. De Raedt. Bayesian Logic Programs. In Proceedings of "Informatiktage2000", Bad Schussenried, Germany, October 2000.
K. Kersting, L. De Raedt. Bayesian Logic Programs. (On invitation). In E. Leopold and M. Kirsten, editors, Proceedings of "Treffen der GIFachgruppe 1.1.3 Maschinelles Lernen" (FGML2000), GMD Report 114, Sankt Augustin, Germany, 2000. (not refereed)
K. Kersting, L. De Raedt. Bayesian Logic Programs. In J. Cussens and A. Frisch, editors, WorkinProgress Reports of the Tenth International Conference on Inductive Logic Programming (ILP 2000), London,U.K., 2000. (online Proceedings)
K. Kersting, L. De Raedt. Bayesian Logic Programs. (On invitation). In F. Furukawa, S. Muggleton, D. Michie, and L. De Raedt, editors, Proceedings of the Seventeenth Machine Intelligence Workshop (MI17), Bury St. Edmunds, Suffolk, U.K., 2000.