[{"publisher":"Springer","language":[{"iso":"eng"}],"type":"book_chapter","doi":"https://link.springer.com/book/10.1007/978-3-662-73474-2","user_id":"264138","date_created":"2026-10-23T12:54:30Z","year":"2026","status":"public","date_updated":"2026-10-23T12:57:50Z","publication":"Künstliche Intelligenz und Data Science in Theorie und Praxis: Von klassischen Algorithmen zu Generativer KI und Agentic AI Konzepte und Realisierung in Unternehmen","author":[{"id":"264138","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/231043426","full_name":"Weisser, Christoph","first_name":"Christoph","orcid":"0000-0003-0616-1027","last_name":"Weisser"}],"editor":[{"last_name":"Gillhuber","first_name":"Andreas","full_name":"Gillhuber, Andreas"},{"first_name":"Göran","last_name":"Kauermann","full_name":"Kauermann, Göran"},{"last_name":"Hauner","first_name":"Wolfgang","full_name":"Hauner, Wolfgang"}],"title":"Von der KI-Plattform zur dialogfähigen Intelligenz: Die Zukunft industrieller Prognosen","_id":"7227"},{"citation":{"short":"F. Lukassen, C. Weisser, A. Silbersdorff, Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges, 2026.","ieee":"F. Lukassen, C. Weisser, and A. Silbersdorff, Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges. 2026.","mla":"Lukassen, Fabian, et al. Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges. 2026.","alphadin":"Lukassen, Fabian ; Weisser, Christoph ; Silbersdorff, Alexander: Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges, 2026","ama":"Lukassen F, Weisser C, Silbersdorff A. Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges.; 2026.","bibtex":"@book{Lukassen_Weisser_Silbersdorff_2026, title={Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges}, author={Lukassen, Fabian and Weisser, Christoph and Silbersdorff, Alexander}, year={2026} }","chicago":"Lukassen, Fabian, Christoph Weisser, and Alexander Silbersdorff. Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges, 2026.","apa":"Lukassen, F., Weisser, C., & Silbersdorff, A. (2026). Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges."},"_id":"7217","author":[{"full_name":"Lukassen, Fabian","last_name":"Lukassen","first_name":"Fabian"},{"orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/230070257","full_name":"Weisser, Christoph","first_name":"Christoph","last_name":"Weisser","orcid":"0000-0003-0616-1027","id":"264138"},{"full_name":"Silbersdorff, Alexander","last_name":"Silbersdorff","first_name":"Alexander"}],"title":"Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges","status":"public","date_updated":"2026-10-15T08:13:47Z","date_created":"2026-10-15T07:09:21Z","year":"2026","type":"working_paper","user_id":"261203","language":[{"iso":"eng"}],"abstract":[{"text":"Typed decision models (TDMs) return a probability for each allowed answer instead of generating text, which makes them candidates for cheaply replacing LLM judges that compare two answers. Whether they share the biases of LLM judges is untested. We run the CALM benchmark's bias tests on eight TDMs and seven LLM judges of 2024 and 2026. Every TDM is biased, in three to eight of twelve tests. Emotional tone affects them most, and rewriting the better answer in such a tone lowers the accuracy of a typical TDM from 99% to 42%. TDMs are more biased than 2026 LLM judges. All eight change verdicts when the answers swap places, which the 2026 LLM judges hardly do, and several side with an answer that most people allegedly prefer or that a famous model allegedly wrote. Yet both kinds of judge are tripped up by the same prompts. The TDMs' probabilities show that most tests mainly flip verdicts the TDM was unsure of, and that good calibration does not protect against bias. As TDMs are more consistent and 37 times cheaper than 2026 LLM judges, our results suggest they can be attractive judges if their biases are tested for the task.","lang":"eng"}]},{"title":"TAME: Element-wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction","oa":"1","type":"working_paper","doi":"10.26434/chemrxiv.15008270/v2","year":"2026","author":[{"full_name":"Schiller, Robert P.","first_name":"Robert P.","last_name":"Schiller"},{"id":"264138","full_name":"Weisser, Christoph","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/226396719","orcid":"0000-0003-0616-1027","last_name":"Weisser","first_name":"Christoph"},{"first_name":"Klaus-Robert","last_name":"Müller","full_name":"Müller, Klaus-Robert"},{"full_name":"Ochsenfeld, Christian","last_name":"Ochsenfeld","first_name":"Christian"},{"last_name":"Semnani","first_name":"Parastoo","full_name":"Semnani, Parastoo"}],"citation":{"apa":"Schiller, R. P., Weisser, C., Müller, K.-R., Ochsenfeld, C., & Semnani, P. (2026). TAME: Element-wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction. https://doi.org/10.26434/chemrxiv.15008270/v2","chicago":"Schiller, Robert P., Christoph Weisser, Klaus-Robert Müller, Christian Ochsenfeld, and Parastoo Semnani. TAME: Element-Wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction, 2026. https://doi.org/10.26434/chemrxiv.15008270/v2.","short":"R.P. Schiller, C. Weisser, K.-R. Müller, C. Ochsenfeld, P. Semnani, TAME: Element-Wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction, 2026.","ieee":"R. P. Schiller, C. Weisser, K.-R. Müller, C. Ochsenfeld, and P. Semnani, TAME: Element-wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction. 2026.","mla":"Schiller, Robert P., et al. TAME: Element-Wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction. 2026, doi:10.26434/chemrxiv.15008270/v2.","alphadin":"Schiller, Robert P. ; Weisser, Christoph ; Müller, Klaus-Robert ; Ochsenfeld, Christian ; Semnani, Parastoo: TAME: Element-wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction, 2026","ama":"Schiller RP, Weisser C, Müller K-R, Ochsenfeld C, Semnani P. TAME: Element-Wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction.; 2026. doi:10.26434/chemrxiv.15008270/v2","bibtex":"@book{Schiller_Weisser_Müller_Ochsenfeld_Semnani_2026, title={TAME: Element-wise Mixture-of-Experts Fusion for Reliable and Interpretable Molecular Property Prediction}, DOI={10.26434/chemrxiv.15008270/v2}, author={Schiller, Robert P. and Weisser, Christoph and Müller, Klaus-Robert and Ochsenfeld, Christian and Semnani, Parastoo}, year={2026} }"},"_id":"7152","main_file_link":[{"open_access":"1"}],"abstract":[{"text":"Property prediction for molecules and materials is bottlenecked by label scarcity, and in this regime the operative failure mode is not a low mean but an unpredictable tail: individual training runs collapse. The three dominant molecular representations fail in complementary ways?physicochemical descriptors are exact but fixed, graph neural networks learn topology but degenerate under sparse supervision, and language models supply semantic context but cannot compute exact quantities? yet combining them is itself the learning problem, because modality-level scalar gating commits the whole model to one trust weight per source. We introduce TAME, a tri-modal encoder whose element-wise Mixture-of-Experts router assigns an independent modality mixture to every hidden coordinate, stabilized by a balance?entropy regularizer, a closed-form gate initialization (? = log τ ) that is intended to remove router burn-in, and two-stage self-supervised graph pretraining. On scaffold-split BACE (≈ 1.5k molecules, 100 seeds) we evaluate two fusion topologies, each against its own graph-only control. In both, adding the text and descriptor experts contracts the seed-to-seed distribution of the threshold-free metrics?ROC-AUC σ falls five-fold in the flat topology and 1.6-fold in the hierarchical one, whose control was already the tighter of the two?and the collapse tail reaching ROC-AUC ≈ 0.45 that the weaker single-modality configurations carry is absent from the fused models while accuracy is retained. Each comparison is matched on everything but the fusion stage, isolating its effect. The router allocates a distinct mixture to each coordinate, which no scalar gate can represent, and the entropy weight moves that allocation continuously between graded mixing and a near-binary regime in which each coordinate is claimed outright by one modality. Fused-representation alignment tracks encoder quality without supervision, shifting from text to topology once pretraining lifts the graph embedding out of rank-1 collapse. The same three experts map onto crystal graphs, composition?structure descriptors and literature-mined text. These three modalities recur unchanged across molecular and inorganic chemistry, so TAME is a single interpretable recipe for turning volatile low-data predictors into reliable, self-explaining ones, wherever data are scarce and every failed prediction costs a real experiment.","lang":"eng"}],"language":[{"iso":"eng"}],"user_id":"220548","date_created":"2026-09-10T07:31:07Z","publication_status":"published","status":"public","date_updated":"2026-09-11T08:11:21Z"},{"title":"From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning","related_material":{"link":[{"url":"https://www.hsbi.de/publikationsserver/record/7112","relation":"later_version"}]},"type":"journal_article","publisher":"Arxiv","year":"2026","author":[{"last_name":"Kumar","first_name":"Manish","full_name":"Kumar, Manish"},{"full_name":"Thielmann, Anton Frederik","last_name":"Thielmann","first_name":"Anton Frederik"},{"id":"264138","first_name":"Christoph","last_name":"Weisser","orcid":"0000-0003-0616-1027","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/216498143","full_name":"Weisser, Christoph"},{"first_name":"Benjamin","last_name":"Säfken","full_name":"Säfken, Benjamin"}],"main_file_link":[{"url":"https://arxiv.org/abs/2604.05635v1"}],"_id":"6958","citation":{"ama":"Kumar M, Thielmann AF, Weisser C, Säfken B. From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. Preprint. 2026.","bibtex":"@article{Kumar_Thielmann_Weisser_Säfken_2026, title={From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning}, journal={Preprint}, publisher={Arxiv}, author={Kumar, Manish and Thielmann, Anton Frederik and Weisser, Christoph and Säfken, Benjamin}, year={2026} }","mla":"Kumar, Manish, et al. “From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning.” Preprint, Arxiv, 2026.","ieee":"M. Kumar, A. F. Thielmann, C. Weisser, and B. Säfken, “From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning,” Preprint, 2026.","alphadin":"Kumar, Manish ; Thielmann, Anton Frederik ; Weisser, Christoph ; Säfken, Benjamin: From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. In: Preprint, Arxiv (2026)","short":"M. Kumar, A.F. Thielmann, C. Weisser, B. Säfken, Preprint (2026).","chicago":"Kumar, Manish, Anton Frederik Thielmann, Christoph Weisser, and Benjamin Säfken. “From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning.” Preprint, 2026.","apa":"Kumar, M., Thielmann, A. F., Weisser, C., & Säfken, B. (2026). From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. Preprint."},"user_id":"220548","language":[{"iso":"eng"}],"publication":"Preprint","date_updated":"2026-08-21T12:29:02Z","status":"public","publication_status":"published","date_created":"2026-06-01T14:03:54Z"},{"citation":{"chicago":"Kumar, Manish, Anton Frederik Thielmann, Christoph Weisser, and Benjamin Säfken. “From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning.” Transactions on Machine Learning Research, 2026. https://doi.org/10.48550/arXiv.2604.05635.","apa":"Kumar, M., Thielmann, A. F., Weisser, C., & Säfken, B. (2026). From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2604.05635","alphadin":"Kumar, Manish ; Thielmann, Anton Frederik ; Weisser, Christoph ; Säfken, Benjamin: From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. In: Transactions on Machine Learning Research, arxiv (2026)","ieee":"M. Kumar, A. F. Thielmann, C. Weisser, and B. Säfken, “From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning,” Transactions on Machine Learning Research, 2026.","mla":"Kumar, Manish, et al. “From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning.” Transactions on Machine Learning Research, arxiv, 2026, doi:10.48550/arXiv.2604.05635.","short":"M. Kumar, A.F. Thielmann, C. Weisser, B. Säfken, Transactions on Machine Learning Research (2026).","bibtex":"@article{Kumar_Thielmann_Weisser_Säfken_2026, title={From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning}, DOI={10.48550/arXiv.2604.05635}, journal={Transactions on Machine Learning Research}, publisher={arxiv}, author={Kumar, Manish and Thielmann, Anton Frederik and Weisser, Christoph and Säfken, Benjamin}, year={2026} }","ama":"Kumar M, Thielmann AF, Weisser C, Säfken B. From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. Transactions on Machine Learning Research. 2026. doi:10.48550/arXiv.2604.05635"},"_id":"7112","author":[{"last_name":"Kumar","first_name":"Manish","full_name":"Kumar, Manish"},{"full_name":"Thielmann, Anton Frederik","first_name":"Anton Frederik","last_name":"Thielmann"},{"id":"264138","full_name":"Weisser, Christoph","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/224483678","orcid":"0000-0003-0616-1027","last_name":"Weisser","first_name":"Christoph"},{"last_name":"Säfken","first_name":"Benjamin","full_name":"Säfken, Benjamin"}],"date_created":"2026-08-20T17:25:46Z","publication":"Transactions on Machine Learning Research","date_updated":"2026-08-21T12:28:04Z","status":"public","language":[{"iso":"eng"}],"user_id":"220548","related_material":{"link":[{"relation":"earlier_version","url":"https://www.hsbi.de/publikationsserver/record/6958"}]},"quality_controlled":"1","title":"From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning","year":"2026","publisher":"arxiv","type":"journal_article","doi":"10.48550/arXiv.2604.05635"},{"date_created":"2026-08-20T17:19:49Z","year":"2026","status":"public","date_updated":"2026-08-21T12:16:32Z","publication":"arXiv:2608.11753","language":[{"iso":"eng"}],"doi":"10.48550/arXiv.2608.11753 ","type":"journal_article","user_id":"220548","_id":"7110","citation":{"bibtex":"@article{Schlee_Lukassen_Weisser_2026, title={LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification}, DOI={10.48550/arXiv.2608.11753 }, journal={arXiv:2608.11753}, author={Schlee, Michael and Lukassen, Fabian and Weisser, Christoph}, year={2026} }","ama":"Schlee M, Lukassen F, Weisser C. LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification. arXiv:260811753. 2026. doi:10.48550/arXiv.2608.11753 ","alphadin":"Schlee, Michael ; Lukassen, Fabian ; Weisser, Christoph: LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification. In: arXiv:2608.11753 (2026)","ieee":"M. Schlee, F. Lukassen, and C. Weisser, “LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification,” arXiv:2608.11753, 2026.","mla":"Schlee, Michael, et al. “LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification.” ArXiv:2608.11753, 2026, doi:10.48550/arXiv.2608.11753 .","short":"M. Schlee, F. Lukassen, C. Weisser, ArXiv:2608.11753 (2026).","apa":"Schlee, M., Lukassen, F., & Weisser, C. (2026). LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification. ArXiv:2608.11753. https://doi.org/10.48550/arXiv.2608.11753 ","chicago":"Schlee, Michael, Fabian Lukassen, and Christoph Weisser. “LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification.” ArXiv:2608.11753, 2026. https://doi.org/10.48550/arXiv.2608.11753 ."},"author":[{"full_name":"Schlee, Michael","last_name":"Schlee","first_name":"Michael"},{"full_name":"Lukassen, Fabian","first_name":"Fabian","last_name":"Lukassen"},{"id":"264138","full_name":"Weisser, Christoph","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/224400435","last_name":"Weisser","orcid":"0000-0003-0616-1027","first_name":"Christoph"}],"title":"LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification"},{"author":[{"full_name":"Lukassen, Fabian","first_name":"Fabian","last_name":"Lukassen"},{"id":"264138","first_name":"Christoph","last_name":"Weisser","orcid":"0000-0003-0616-1027","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/220971488","full_name":"Weisser, Christoph"},{"first_name":"Thomas","last_name":"Kneib","full_name":"Kneib, Thomas"},{"full_name":"Silbersdorff, Alexander","first_name":"Alexander","last_name":"Silbersdorff"}],"_id":"7066","citation":{"chicago":"Lukassen, Fabian, Christoph Weisser, Thomas Kneib, and Alexander Silbersdorff. “CAFE: A Compound-AI Factorial Evaluation Framework.” Preprint, 2026. https://doi.org/10.48550/arXiv.2607.10380.","apa":"Lukassen, F., Weisser, C., Kneib, T., & Silbersdorff, A. (2026). CAFE: A Compound-AI Factorial Evaluation Framework. Preprint. https://doi.org/10.48550/arXiv.2607.10380","short":"F. Lukassen, C. Weisser, T. Kneib, A. Silbersdorff, Preprint (2026).","ieee":"F. Lukassen, C. Weisser, T. Kneib, and A. Silbersdorff, “CAFE: A Compound-AI Factorial Evaluation Framework,” Preprint, 2026.","mla":"Lukassen, Fabian, et al. “CAFE: A Compound-AI Factorial Evaluation Framework.” Preprint, arxiv, 2026, doi:10.48550/arXiv.2607.10380.","alphadin":"Lukassen, Fabian ; Weisser, Christoph ; Kneib, Thomas ; Silbersdorff, Alexander: CAFE: A Compound-AI Factorial Evaluation Framework. In: Preprint, arxiv (2026)","ama":"Lukassen F, Weisser C, Kneib T, Silbersdorff A. CAFE: A Compound-AI Factorial Evaluation Framework. Preprint. 2026. doi:10.48550/arXiv.2607.10380","bibtex":"@article{Lukassen_Weisser_Kneib_Silbersdorff_2026, title={CAFE: A Compound-AI Factorial Evaluation Framework}, DOI={10.48550/arXiv.2607.10380}, journal={Preprint}, publisher={arxiv}, author={Lukassen, Fabian and Weisser, Christoph and Kneib, Thomas and Silbersdorff, Alexander}, year={2026} }"},"user_id":"220548","language":[{"iso":"eng"}],"status":"public","date_updated":"2026-07-17T07:51:15Z","publication":"Preprint","date_created":"2026-07-16T11:47:16Z","title":"CAFE: A Compound-AI Factorial Evaluation Framework","type":"journal_article","doi":"10.48550/arXiv.2607.10380","publisher":"arxiv","year":"2026"},{"title":"Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung","quality_controlled":"1","oa":"1","article_type":"letter_note","year":"2026","publisher":"Springer","type":"journal_article","doi":"10.1007/s11943-026-00372-0","alternative_id":["7067"],"citation":{"alphadin":"Berger, Ursula ; Biehler, Rolf ; Binder, Karin ; Elmer, Christina ; Ertz, Florian ; Hotz, Thomas ; Huber, Sarah ; Ickstadt, Katja ; u. a.: Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung. In: AStA Wirtschafts- und Sozialstatistisches Archiv, Springer (2026)","mla":"Berger, Ursula, et al. “Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung.” AStA Wirtschafts- und Sozialstatistisches Archiv, Springer, 2026, doi:10.1007/s11943-026-00372-0.","ieee":"U. Berger et al., “Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung,” AStA Wirtschafts- und Sozialstatistisches Archiv, 2026.","short":"U. Berger, R. Biehler, K. Binder, C. Elmer, F. Ertz, T. Hotz, S. Huber, K. Ickstadt, G. Kauermann, H. Küchenhoff, K. Lübke, R. Münnich, K. Schüller, T. Skill, C. Weihs, H. Weinert, C. Weisser, AStA Wirtschafts- und Sozialstatistisches Archiv (2026).","bibtex":"@article{Berger_Biehler_Binder_Elmer_Ertz_Hotz_Huber_Ickstadt_Kauermann_Küchenhoff_et al._2026, title={Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung}, DOI={10.1007/s11943-026-00372-0}, journal={AStA Wirtschafts- und Sozialstatistisches Archiv}, publisher={Springer}, author={Berger, Ursula and Biehler, Rolf and Binder, Karin and Elmer, Christina and Ertz, Florian and Hotz, Thomas and Huber, Sarah and Ickstadt, Katja and Kauermann, Göran and Küchenhoff, Helmut and et al.}, year={2026} }","ama":"Berger U, Biehler R, Binder K, et al. Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung. AStA Wirtschafts- und Sozialstatistisches Archiv. 2026. doi:10.1007/s11943-026-00372-0","apa":"Berger, U., Biehler, R., Binder, K., Elmer, C., Ertz, F., Hotz, T., … Weisser, C. (2026). Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung. AStA Wirtschafts- und Sozialstatistisches Archiv. https://doi.org/10.1007/s11943-026-00372-0","chicago":"Berger, Ursula, Rolf Biehler, Karin Binder, Christina Elmer, Florian Ertz, Thomas Hotz, Sarah Huber, et al. “Daten, Künstliche Intelligenz und Evidenz - neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung.” AStA Wirtschafts- und Sozialstatistisches Archiv, 2026. https://doi.org/10.1007/s11943-026-00372-0."},"_id":"6924","main_file_link":[{"open_access":"1"}],"author":[{"full_name":"Berger, Ursula","first_name":"Ursula","last_name":"Berger"},{"last_name":"Biehler","first_name":"Rolf","full_name":"Biehler, Rolf"},{"last_name":"Binder","first_name":"Karin","full_name":"Binder, Karin"},{"first_name":"Christina","last_name":"Elmer","full_name":"Elmer, Christina"},{"full_name":"Ertz, Florian","first_name":"Florian","last_name":"Ertz"},{"full_name":"Hotz, Thomas","last_name":"Hotz","first_name":"Thomas"},{"last_name":"Huber","first_name":"Sarah","full_name":"Huber, Sarah"},{"first_name":"Katja","last_name":"Ickstadt","full_name":"Ickstadt, Katja"},{"full_name":"Kauermann, Göran","first_name":"Göran","last_name":"Kauermann"},{"first_name":"Helmut","last_name":"Küchenhoff","full_name":"Küchenhoff, Helmut"},{"first_name":"Karsten","last_name":"Lübke","full_name":"Lübke, Karsten"},{"full_name":"Münnich, Ralf","first_name":"Ralf","last_name":"Münnich"},{"last_name":"Schüller","first_name":"Katharina","full_name":"Schüller, Katharina"},{"full_name":"Skill, Thomas","first_name":"Thomas","last_name":"Skill"},{"full_name":"Weihs, Claus","last_name":"Weihs","first_name":"Claus"},{"first_name":"Henrike","last_name":"Weinert","full_name":"Weinert, Henrike"},{"id":"264138","full_name":"Weisser, Christoph","first_name":"Christoph","orcid":"0000-0003-0616-1027","last_name":"Weisser"}],"date_created":"2026-05-28T11:24:42Z","publication_status":"published","status":"public","publication":"AStA Wirtschafts- und Sozialstatistisches Archiv","date_updated":"2026-07-16T15:20:37Z","publication_identifier":{"issn":["1863-8155"],"eissn":["1863-8163"]},"language":[{"iso":"ger"}],"abstract":[{"lang":"ger","text":"Das Positionspapier formuliert neun Thesen zur Weiterentwicklung der Statistikausbildung im Kontext von Data Science und Künstlicher Intelligenz. Im Zentrum stehen Data & Statistical Literacy, Datenqualität und -ethik, die Verbindung von Statistik und Machine Learning sowie die Stärkung einer eigenständigen Statistikdidaktik.\r\n\r\nDie Diskussionsbeiträge erweitern diese Perspektiven: Christina Elmer betont Wissenschaftskommunikation und AI Literacy, Helmut Küchenhoff projektbasiertes Lernen und Datenschutz, Christoph Weisser die industrielle Praxis und organisationsweite Datenkompetenz, Göran Kauermann die Verzahnung von Statistik und Data Science, und Rolf Biehler sowie Karin Binder die didaktische und institutionelle Weiterentwicklung.\r\n\r\nDie Replik greift diese Impulse auf und unterstreicht die interdisziplinäre Verantwortung für eine zukunftsfähige Statistikausbildung."},{"text":"The position paper formulates nine theses on the future development of statistics education in the context of Data Science and Artificial Intelligence. It emphasizes Data & Statistical Literacy, data quality and ethics, the integration of statistics and machine learning, and the strengthening of an independent didactics of statistics.\r\n\r\nThe discussion contributions broaden these perspectives: Christina Elmer highlights science communication and AI literacy; Helmut Küchenhoff emphasizes project-based learning and data protection; Christoph Weisser stresses industrial practice and organization-wide data competence; Göran Kauermann reflects on the interplay between statistics and data science; and Rolf Biehler and Karin Binder focus on didactical and institutional development.\r\n\r\nThe rejoinder takes up these impulses and underlines the interdisciplinary responsibility for shaping future-oriented statistics education.","lang":"eng"}],"user_id":"220548"},{"year":"2026","type":"journal_article","doi":"10.48550/arXiv.2603.09085","title":"Not All News Is Equal: Topic- and Event-Conditional Sentiment From Finetuned LLMs for Aluminum Price Forecasting","oa":"1","date_created":"2026-06-01T14:03:16Z","publication_status":"published","status":"public","publication":"LREC 2026 - The Fifteenth biennial Language Resources and Evaluation Conference - Financial Narrative Processing (FNP) Workshop","date_updated":"2026-07-16T11:53:34Z","language":[{"iso":"eng"}],"user_id":"264138","_id":"6957","citation":{"bibtex":"@article{Amorin_Python_Weisser_2026, title={Not All News Is Equal: Topic- and Event-Conditional Sentiment From Finetuned LLMs for Aluminum Price Forecasting}, DOI={10.48550/arXiv.2603.09085}, journal={LREC 2026 - The Fifteenth biennial Language Resources and Evaluation Conference - Financial Narrative Processing (FNP) Workshop}, author={Amorin, Alvaro Paredes and Python, Andre and Weisser, Christoph}, year={2026} }","ama":"Amorin AP, Python A, Weisser C. 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