World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
62
Citations
12895
World Ranking
2953
National Ranking
135

Overview

Kristian Kersting is affiliated with the Technical University of Darmstadt in Germany, where they concentrate their research efforts in the field of Computer Science, contributing extensively across various subfields and topics.

Their work spans several subfields of study, including Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems, and Economics and Econometrics.

The principal topics Kristian Kersting engages with include:

  • Explainable Artificial Intelligence (XAI)
  • Topic Modeling
  • Adversarial Robustness in Machine Learning
  • Bayesian Modeling and Causal Inference
  • Machine Learning and Data Classification
  • Natural Language Processing Techniques
  • Privacy-Preserving Technologies in Data

Kersting has produced numerous publications with notable venues where their research frequently appears. These venues include arXiv (Cornell University), Frontiers in Artificial Intelligence, Nature Machine Intelligence, Proceedings of the International AAAI Conference on Web and Social Media, and the Journal of Artificial Intelligence Research.

Some recent publications are:

  • Trained models, code, result data, and Optuna study data from "Hybrid quantum or purely classical? Assessing the utility of quantum feature embeddings" (2024, arXiv (Cornell University))
  • Large pre-trained language models contain human-like biases of what is right and wrong to do (2022, Nature Machine Intelligence)
  • DeepDB (2020, Proceedings of the VLDB Endowment)
  • Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash (2022, 2022 ACM Conference on Fairness, Accountability, and Transparency)
  • Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials (2020, Computational Mechanics)

Their frequent co-authors include Devendra Singh Dhami, Patrick Schramowski, Wolfgang Stammer, F. Friedrich, and Matej Zečević, reflecting sustained collaboration over many publications.

Kersting has also contributed to academic literature published by Springer Science+Business Media, authoring the book "Machine Learning and Knowledge Discovery in Databases" in 2021, with at least two editions or volumes recorded.

Best Publications

  • Probabilistic inductive logic programming

    Luc De Raedt;Kristian Kersting

  • Most likely heteroscedastic Gaussian process regression

    Kristian Kersting;Christian Plagemann;Patrick Pfaff;Wolfram Burgard

  • TUDataset: A collection of benchmark datasets for learning with graphs.

    Christopher Morris;Nils M. Kriege;Franka Bause;Kristian Kersting

  • Interpreting Bayesian Logic Programs

    Kristian Kersting;Luc De Raedt;Stefan Kramer

  • Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

    Luc De Raedt;Kristian Kersting;Sriraam Natarajan

  • Propagation kernels: efficient graph kernels from propagated information

    Marion Neumann;Roman Garnett;Christian Bauckhage;Kristian Kersting

  • Bayesian Logic Programs

    Kristian Kersting;Luc De Raedt

  • Lifted probabilistic inference with counting formulas

    Brian Milch;Luke S. Zettlemoyer;Kristian Kersting;Michael Haimes

  • Probabilistic logic learning

    Luc De Raedt;Kristian Kersting

  • Bayesian Logic Programming: Theory and Tool

    Kristian Kersting;Luc De Raedt

  • DeepDB: learn from data, not from queries!

    Benjamin Hilprecht;Andreas Schmidt;Moritz Kulessa;Alejandro Molina

  • Predicting player churn in the wild

    Fabian Hadiji;Rafet Sifa;Anders Drachen;Christian Thurau

  • Towards Combining Inductive Logic Programming with Bayesian Networks

    Kristian Kersting;Luc De Raedt

  • Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis

    Christoph Römer;Mirwaes Wahabzada;Agim Ballvora;Francisco Pinto

  • Explanatory Interactive Machine Learning

    Stefano Teso;Kristian Kersting

  • Gradient-based boosting for statistical relational learning: The relational dependency network case

    Sriraam Natarajan;Tushar Khot;Kristian Kersting;Bernd Gutmann

  • Making deep neural networks right for the right scientific reasons by interacting with their explanations

    Patrick Schramowski;Wolfgang Stammer;Stefano Teso;Anna Brugger

  • Counting belief propagation

    Kristian Kersting;Babak Ahmadi;Sriraam Natarajan

  • Bellman goes relational

    Kristian Kersting;Martijn Van Otterlo;Luc De Raedt

  • Metro maps of plant disease dynamics--automated mining of differences using hyperspectral images.

    Mirwaes Wahabzada;Anne-Katrin Mahlein;Christian Bauckhage;Ulrike Steiner

  • Making deep neural networks right for the right scientific reasons by interacting with their explanations

    Patrick Schramowski;Wolfgang Stammer;Stefano Teso;Anna Brugger

Frequent Co-Authors

Christian Bauckhage
Christian Bauckhage University of Bonn
Luc De Raedt
Luc De Raedt KU Leuven
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Wolfram Burgard
Wolfram Burgard University of Technology Nuremberg
Petra Mutzel
Petra Mutzel University of Bonn
Ulrike Steiner
Ulrike Steiner University of Bonn
Scott Sanner
Scott Sanner University of Toronto
Uwe Rascher
Uwe Rascher Forschungszentrum Jülich
Erich-Christian Oerke
Erich-Christian Oerke University of Bonn

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