Publications

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, und Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL

Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, und Martin Potthast. CauseNet: Towards a Causality Graph Extracted from the Web. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 3023–3030, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: causality extraction, graph, information knowledge] URL

Matti Wiegmann, Jennifer Rakete, Magdalena Wolska, Benno Stein, und Martin Potthast. If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level. arXiv, 2024. [PUMA: (cs.CL), (cs.CY), Computation Computer Computers FOS: Language Society and information sciences sciences,] URL

Lukas Gienapp, Maik Fröbe, Matthias Hagen, und Martin Potthast. The Impact of Negative Relevance Judgments on NDCG. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2037–2040, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: cumulated discounted evaluation, gain, information judgements, normalized relevance reliability, retrieval, stability] URL

Lukas Gienapp, Benno Stein, Matthias Hagen, und Martin Potthast. Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2033–2036, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: cumulative difficulty discounted evaluation, gain, information normalized retrieval, topic] URL