publications

2007

  1. K. Kersting, B. Milch, L.S. Zettlemoyer, M. Haimes, L. Pack Kaelbling. Reasoning about Large Populations with Lifted Probabilistic Inference. 2-page abstract in L. Getoor, R. Gottardo, K. Murphy, E. Xing, editors, Working Notes of the NIPS-07 Workshop on Statistical Model of Networks, December 8th, 2007, Whistler, BC, Canada, 2007.

  2. 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 Multi-Relational Data Mining (MRDM-07) at ECML/PKDD-07, 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 1-3, 2007.

  3. 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 Multi-Relational Data Mining (MRDM-07) at ECML/PKDD-07, 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 1-3, 2007.

  4. S. Sanner, K. Kersting. Symbolic Dynamic Programming. Chapter to appear in C. Sammut, editor, Encyclopedia of Machine Learning, Springer-Verlag, 2007.[draft]

  5. 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 (IPM-07) at ICML-07, Corvallis, OR, USA, June 24, 2007.[draft]

  6. 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.

  7. K. Kersting, C. Plagemann, P. Pfaff, W. Burgard. Most-Likely Heteroscedastic Gaussian Process Regression. In the Proceedings of the 24th Annual International Conference on Machine Learning (ICML-07), Corvallis, OR, USA, June 20-24, 2007.[draft]

  8. 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 (RSS-07), Atlanta, GA, USA, June 27-30, 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 19-22, 2007.

  9. N. Landwehr, K. Kersting, L. De Raedt. nFOIL: Integrating Naive Bayes and FOIL. Journal of Machine Learning Research (JMLR) 8(Mar):481-507, 2007. [draft][software]

  10. L. De Raedt, K. Kersting, A. Kimmig, K. Revoredo, H. Toivonen. Revising Probabilistic Prolog Programs. Accepted for publication in the Machine Learning Journal (MLJ), ILP-2006 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 (ILP-06), Santiago, Spain, August 24-27, 2006.

2006

  1. 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 (ILP-06), Santiago, Spain, August 24-27, 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 (ILP-06), Santiago, Spain, August 24-27, 2006.

    A short paper also appeared as Relational Sequence Alignment in T. Gaertner, G. C. Garriga, T. Meinl, editors, Working Notes of the ECML-06 Workshop on Mining and Learning with Graphs (MLG 2006), Berlin, Germany, September 18th, 2006. [draft]

  2. 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.

  3. 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 ECML-06 Workshop on Mining and Learning with Graphs (MLG 2006), Berlin, Germany, September 18th, 2006.[draft]

  4. 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 (ECML-2006), pages 174-185, Berlin, Germany, September 18-22, 2006. [draft][software] Best student paper award

  5. 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 (ECML-2006),pages 114-125, Berlin, Germany, September 18-22, 2006. [draft]

  6. 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 (IROS-06), Bejing, China, October 9-15, 2006 [draft]

    A slightly different version appeared in H.-M. Gross, editor, Special Issue "Lernen und Selbstorganisation von Verhalten", Kuenstliche Intelligenz (3/06):12-18.

  7. M. Jaeger, K. Kersting, L. De Raedt. Expressivity Analysis for PL-Languages. In A. Fern, L. Getoor, and B. Milch, editors, Working Notes of the ICML-06 Workshop "Open Problems in Statistical Relational Learning" (SRL-06), Pittsburgh, USA, June 29, 2006. [draft]

  8. 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 (ICRA-06), Walt Disney World Resort in Orlando, Florida, USA, May 15-19, 2006. [draft]

  9. K.Kersting, L. De Raedt, T. Raiko. Logical Hidden Markov Models. Journal of Artificial Intelligence Research, Volume 25, pages 425-456, 2006. [draft]

  10. 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 1-58603-674-2, LCCN 2006932504. [@IOS Press]
     
    This is a reprint of my Ph.D Thesis, Institute for Computer Science, Albert-Ludwigs 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):389-390, 2006.

2005

  1. 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]

  2. T. Guerel, K. Kersting. On the Trade-Off 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 (MGTS-05), Porto, Portugal, October 7, 2005. [draft]

  3. 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 (UAI-05), Edinburgh, Scotland, July 26-29, 2005. [draft]

    4. 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 KDD-Workshop on Multi-Relational Data Mining (MRDM-03), Washington, DC, USA, August 27, 2003.[draft]

  4. 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 (AAAI-05), pages 795-800, Pittsburgh, Pennsylvania, USA, July 9-13, 2005. [draft]

  5. L. De Raedt, K. Kersting, S. Torge. Towards Learning Stochastic Logic Programs from Proof-Banks. In M. Veloso and S. Kambhampati, editors, Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), pages 752-757, Pittsburgh, Pennsylvania, USA, July 9-13, 2005. [draft]

2004

  1. L. De Raedt, K. Kersting. Probabilistic Inductive Logic Programming. Invited paper in S. Ben-David, J. Case and A. Maruoka, editors, Proceedings of the 15th International Conference on Algorithmic Learning Theory (ALT-2004), pages 19-36. Padova, Italy, October 2-5, 2004. [draft]

  2. 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 (PKDD-2004), pages 549-551. Pisa, Italy, September 20-25, 2004. [draft][software]

  3. 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 (ECML-2004), pages 205 - 216. Pisa, Italy, September 20-25, 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'Alche-Buc, P. Long. December (Friday) 13, 2002, Vancouver, Canada.
    [two-page draft]

  4. 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 (ILP-2004), pages 180-197. Porto, Portugal, September 6-8, 2004.[draft]

    An earlier version introducing LOMDPs and showing some experiments with "logical Q-learning" appeared as Logical Markov Decision Programs in L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-03), pp. 63-70, August 11, Acapulco, Mexico, 2003. [draft]

  5. K. Kersting, M. Van Otterlo, L. De Raedt. Bellman goes Relational. In R. Greiner and D. Schuurmans, editors, Proceedings of the Twenty-First International Conference on Machine Learning (ICML-2004), pages 465 - 472. Banff, Alberta, Canada, July 4-8, 2004. [draft]

    A two-page abstract will also appear in L. Schomaker, N. Taatgen, R. Verbruggethe, editors, Proceedings of the Sixtheenth Belgian-Dutch Conference on Artificial Intelligence (BNAIC-04), Groning, The Netherlands, October 21-22, 2004.

  6. M. Van Otterlo, K. Kersting. Challenges for Relational Reinforcement Learning. In the Working Notes of the ICML-2004 Workshop on Relational Reinforcement Learning. P. Tadepalli, R. Givan, K. Driessens, editors. Banff, Alberta, Canada, July 8, 2004. [draft]

  7. K. Kersting, N. Landwehr. Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm. Chapter (pp. 235-254) 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 (PGM-02), pp. 89-98, November 6-8, 2002, Cuenca, Spain. [draft]

2003

  1. L. De Raedt, K. Kersting. Probabilistic Logic Learning. In ACM-SIGKDD Explorations, special issue on Multi-Relational Data Mining, S. Dzeroski and L. De Raedt, editors, Vol. 5(1), pp. 31-48, July 2003. [draft]

  2. 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 (ECML-2003), pp. 133-144, Cavtat, Croatia, September 22-26, 2003. [draft]

  3. K. Kersting. Representational power of probabilistic-logical models: From upgrading to downgrading. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-03), pp. 61-62, August 11, Acapulco, Mexico, 2003.[draft]

  4. 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 (PSB-2003), pp. 192-203, January 3-7 2003, Kauai, Hawaii, USA. [draft]

    Extended abstract in Stan Matwin and Claude Sammut, editors, Work-in-Progress Reports of the Twelfth International Conference on Inductive Logic Programming (ILP -2002),Sydney, Australia, July 9-11, 2002. [draft]

    Technical Report No. 175, Institute for Computer Science, University of Freiburg, Germany, June 2002.[.ps.gz]

2002

  1. T. Raiko, K. Kersting, J. Karhunen, L. De Raedt. Bayesian Learning of Logical Hidden Markov Models. In Proceedings of the Finnish AI conference (STeP-2002), pp. 64-71, 15-17 December 2002, Oulu, Finland. [draft]

  2. 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 (PGM-02), pp. 99-107, November 6-8, 2002, Cuenca, Spain. [draft]

  3. 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]

  4. S. Ganzert, J. Guttmann, K. Kersting, R. Kuhlen, C. Putensen, M. Sydow, S. Kramer. Analysis of Respiratory Pressure-Volume 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(1-2), pp. 69-86, Sept. 2002. [draft]

2001

  1. 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 (ILP-2001),pages 118-131, LNAI 2157, Springer, Strasbourg, France, September 2001. [draft]

  2. 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 (ILP-2001), pages 104 - 117, LNAI 2157, Springer, Strasbourg, France, September 2001.
    [draft]

  3. 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

  1. K. Kersting, L. De Raedt, S. Kramer. Interpreting Bayesian Logic Programs. In L. Getoor and D. Jensen, editors, Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, Technical Report WS-00-06, AAAI Press, Austin/Texas, USA, 2000. [draft]


    2. Previous draft mainly differ in stressing different aspects of Bayesian logic programs: K. Kersting, L. De Raedt. Bayesian Logic Programs. In Proceedings of "Informatiktage-2000", 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 GI-Fachgruppe 1.1.3 Maschinelles Lernen" (FGML-2000), GMD Report 114, Sankt Augustin, Germany, 2000.  (not refereed)

    K. Kersting, L. De Raedt. Bayesian Logic Programs. In J. Cussens and A. Frisch, editors, Work-in-Progress 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 (MI-17), Bury St. Edmunds, Suffolk, U.K., 2000.

list of publications at

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