Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence. - Anton Thielmann; Quentin Seifert; Arik Reuter; Elisabeth Bergherr; Benjamin Säfken (CoRR, 2023)

Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data. - Christoph Weisser; Christoph Gerloff; Anton Thielmann; Andre Python; Arik Reuter; Thomas Kneib; Benjamin Säfken (Comput. Stat., 2023)

STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module. - Anton Thielmann; Arik Reuter; Christoph Weisser; Gillian Kant; Manish Kumar; Benjamin Säfken (ACL (Short Papers), 2024)

Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion. - Anton Thielmann; Arik Reuter; Quentin Seifert; Elisabeth Bergherr; Benjamin Säfken (Comput. Linguistics, 2024)

GPTopic: Dynamic and Interactive Topic Representations. - Arik Reuter; Anton Thielmann; Christoph Weisser; Sebastian Fischer 004; Benjamin Säfken (CoRR, 2024)

Probabilistic Topic Modelling with Transformer Representations. - Arik Reuter; Anton Thielmann; Christoph Weisser; Benjamin Säfken; Thomas Kneib (CoRR, 2024)

Neural Additive Image Model: Interpretation through Interpolation. - Arik Reuter; Anton Thielmann; Benjamin Säfken (CoRR, 2024)

Mambular: A Sequential Model for Tabular Deep Learning. - Anton Frederik Thielmann; Manish Kumar; Christoph Weisser; Arik Reuter; Benjamin Säfken; Soheila Samiee (CoRR, 2024)

Interpretable Additive Tabular Transformer Networks. - Anton Frederik Thielmann; Arik Reuter; Thomas Kneib; David Rügamer; Benjamin Säfken (Trans. Mach. Learn. Res., 2024)

Can Transformers Learn Full Bayesian Inference in Context? - Arik Reuter; Tim G. J. Rudner; Vincent Fortuin; David Rügamer (CoRR, 2025)

Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks. - Anton Thielmann; Arik Reuter; Benjamin Säfken (CoRR, 2025)

Position: The Future of Bayesian Prediction Is Prior-Fitted. - Samuel Müller 005; Arik Reuter; Noah Hollmann; David Rügamer; Frank Hutter (CoRR, 2025)

Do-PFN: In-Context Learning for Causal Effect Estimation. - Jake Robertson; Arik Reuter; Siyuan Guo; Noah Hollmann; Frank Hutter; Bernhard Schölkopf (CoRR, 2025)

Probabilistic Topic Modeling With Transformer Representations. - Arik Reuter; Anton Thielmann; Christoph Weisser; Benjamin Säfken; Thomas Kneib (IEEE Trans. Neural Networks Learn. Syst., 2025)