K
Kristian Kersting
Researcher at Technische Universität Darmstadt
Publications - 462
Citations - 11150
Kristian Kersting is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Computer science & Probabilistic logic. The author has an hindex of 51, co-authored 385 publications receiving 9221 citations. Previous affiliations of Kristian Kersting include Massachusetts Institute of Technology & Fraunhofer Society.
Papers
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Proceedings ArticleDOI
Most likely heteroscedastic Gaussian process regression
TL;DR: This paper follows Goldberg et al.'s approach and model the noise variance using a second GP in addition to the GP governing the noise-free output value, using a Markov chain Monte Carlo method to approximate the posterior noise variance.
Book ChapterDOI
Probabilistic inductive logic programming
Luc De Raedt,Kristian Kersting +1 more
TL;DR: This chapter outlines three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and shows how they can be adapted to cover state-of-the-art statistical relational learning approaches.
Posted Content
TUDataset: A collection of benchmark datasets for learning with graphs.
Christopher Morris,Nils M. Kriege,Franka Bause,Kristian Kersting,Petra Mutzel,Marion Neumann +5 more
TL;DR: The TUDataset for graph classification and regression is introduced, which consists of over 120 datasets of varying sizes from a wide range of applications and provides Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
Proceedings Article
Bayesian Logic Programs
Kristian Kersting,Luc De Raedt +1 more
TL;DR: This work introduces a generalization of Bayesian networks, called Bayesian logic programs, to overcome some of the limitations of propositional logic, and combines Bayesian Networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables.
Proceedings Article
Lifted probabilistic inference with counting formulas
TL;DR: This paper presents a new lifted inference algorithm, C-FOVE, that not only handles counting formulas in its input, but also creates counting formulas for use in intermediate potentials, and achieves asymptotic speed improvements compared to FOVE.