| No. |
Title |
Author |
Year |
| 1 |
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192) |
Cropper, Andrew et al. |
2021 |
| 2 |
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202) |
De Raedt, Luc et al. |
2019 |
| 3 |
Automating Data Science (Dagstuhl Seminar 18401) |
De Bie, Tijl et al. |
2019 |
| 4 |
Constraints, Optimization and Data (Dagstuhl Seminar 14411) |
De Raedt, Luc et al. |
2015 |
| 5 |
Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201) |
De Raedt, Luc et al. |
2011 |
| 6 |
07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis |
De Raedt, Luc et al. |
2008 |
| 7 |
08041 Abstracts Collection -- Recurrent Neural Networks - Models, Capacities, and Applications |
De Raedt, Luc et al. |
2008 |
| 8 |
08041 Summary -- Recurrent Neural Networks - Models, Capacities, and Applications |
De Raedt, Luc et al. |
2008 |
| 9 |
05051 Abstracts Collection -- Probabilistic, Logical and Relational Learning - Towards a Synthesis |
De Raedt, Luc et al. |
2006 |
| 10 |
05051 Executive Summary -- Probabilistic, Logical and Relational Learning - Towards a Synthesis |
De Raedt, Luc et al. |
2006 |
| 11 |
Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting |
Passerini, Andrea et al. |
2006 |
