Prof. Dr.

Kristian Kersting

Technische Universität Darmstadt Computer Science Department and Centre for Cognitive Science
Hochschulstraße 1
64289 Darmstadt

+49 (0)6151 16 24 411 Send e-mail Visit website

Short info

My Artificial Intelligence and Machine Learning lab would like to make computers learn so much about the world, so rapidly and flexibly, as humans. This poses many deep and fascinating scientific problems: How can computers learn with less help from us and data? How can computers reason about and learn with complex data such as graphs and uncertain databases? How can pre-existing knowledge be exploited? How can computers decide autonomously which representation is best for the data at hand? Can learned results be physically plausible or be made understandable by us? How can computers learn together with us in the loop? To this end, we develop novel machine learning (ML) and artificial intelligence (AI) methods, i.e., novel computational methods that contain and combine for example search, logical and probabilistic techniques as well as (deep) (un)supervised and reinforcement learning methods.

Open Science
Yu, Z., Dhami, D. S., & Kersting, K. (2021).
Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits.
arXiv preprint
DOI
Zečević, M., Dhami, D. S., Rothkopf, C. A., & Kersting, K. (2021).
Structural Causal Interpretation Theorem.
arXiv preprint
Articles
Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022).
Large pre-trained language models contain human-like biases of what is right and wrong to do.
Nature Machine Intelligence, 4(3), 258-268.
DOI DOI DOI
Thoma, N., Yu, Z., Ventola, F., & Kersting, K. (2021).
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting.
Proceedings of the 4th Workshop on Tractable Probabilistic Modeling (TPM 2021).
DOI DOI
Ventola, F., Dhami, D. S., & Kersting, K. (2021).
Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.
Proceedings of the 30th International Conference on Inductive Logic Programming (ILP).
DOI DOI
Yu, Z., Ventola, F. G., & Kersting, K. (2021).
Whittle networks: A deep likelihood model for time series.
International Conference on Machine Learning (PMLR), 12177-12186.
DOI
Yu, Z., Zhu, M., Trapp, M., Skryagin, A., & Kersting, K. (2021).
Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression.
Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021).
DOI