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Most likely heteroscedastic Gaussian process regression
- K. Kersting, C. Plagemann, P. Pfaff, W. Burgard
- Computer ScienceICML '07
- 20 June 2007
TLDR
Lifted Probabilistic Inference with Counting Formulas
- Brian Milch, Luke Zettlemoyer, K. Kersting, Michael Haimes, L. Kaelbling
- Computer ScienceAAAI
- 13 July 2008
TLDR
TUDataset: A collection of benchmark datasets for learning with graphs
- Christopher Morris, Nils M. Kriege, Franka Bause, K. Kersting, Petra Mutzel, Marion Neumann
- Computer ScienceArXiv
- 16 July 2020
TLDR
Bayesian Logic Programs
- K. Kersting, L. D. Raedt
- Computer ScienceILP Work-in-progress reports
- 20 April 2001
TLDR
Probabilistic Inductive Logic Programming
- L. D. Raedt, K. Kersting
- Computer ScienceProbabilistic Inductive Logic Programming
- 2 October 2004
TLDR
Gradient-based boosting for statistical relational learning: The relational dependency network case
- Sriraam Natarajan, Tushar Khot, K. Kersting, Bernd Gutmann, J. Shavlik
- Computer ScienceMachine Learning
- 2011
TLDR
DeepDB: Learn from Data, not from Queries!
- Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, K. Kersting, Carsten Binnig
- Computer ScienceProc. VLDB Endow.
- 2 September 2019
TLDR
Towards Combining Inductive Logic Programming with Bayesian Networks
- K. Kersting, L. D. Raedt
- Computer ScienceILP
- 9 September 2001
TLDR
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
- L. D. Raedt, K. Kersting, Sriraam Natarajan, D. Poole
- Computer ScienceStatistical Relational Artificial Intelligence…
- 24 March 2016
TLDR
Propagation kernels: efficient graph kernels from propagated information
- Marion Neumann, R. Garnett, C. Bauckhage, K. Kersting
- Computer ScienceMachine Learning
- 1 February 2016
TLDR
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