Arik Reuter

Max-Planck Institute for Intelligent Systems, Tübingen
University of Cambridge

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I am a PhD student at the University of Cambridge and the Max Planck Institute for Intelligent Systems in Tübingen, working with Bernhard Schölkopf, Adrian Weller and José Miguel Hernández-Lobato.

Currently, my main research interest are Tabular Causal Foundation Models. Causal problems are everywhere; and I am interested in researching how to use foundation models for solving them.

Please feel free to contact me via email.

You can also find me on Google Scholar and GitHub.

Publications

  1. Causal foundation models with observational data and partial graph information
    Use What You Know: Causal Foundation Models with Partial Graphs
    Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, and Bernhard Schölkopf
    In Forty-third International Conference on Machine Learning, 2026
    PDF Code
  2. Numerical and categorical feature encoding in the tabular transformer
    Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
    Anton Thielmann, Arik Reuter, and Benjamin Säfken
    In Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, 2026
    PDF Code
  3. Do-PFN pre-training on causal graphs and observational and interventional data
    Do-PFN: In-Context Learning for Causal Effect Estimation
    Jake Robertson*, Arik Reuter*, Siyuan Guo, Noah Hollmann, Frank Hutter, and Bernhard Schölkopf
    In Advances in Neural Information Processing Systems, 2025
    Spotlight. * Shared first authors.
    PDF Code
  4. In-context text generation compared with sampling a Bayesian posterior
    Can Transformers Learn Full Bayesian Inference in Context?
    Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, and David Rügamer
    In Proceedings of the 42nd International Conference on Machine Learning, 2025
    PDF Code
  5. Prior-fitted learning from synthetic datasets and prediction on new data
    Position: The Future of Bayesian Prediction Is Prior-Fitted
    Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, and Frank Hutter
    In Proceedings of the 42nd International Conference on Machine Learning, 2025
    PDF
  6. Probabilistic Topic Modeling With Transformer Representations
    Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken, and Thomas Kneib
    IEEE Transactions on Neural Networks and Learning Systems, 2025
    PDF (arXiv) Code
  7. STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module
    Anton Thielmann, Arik Reuter, Benjamin Säfken, Christoph Weisser, Manish Kumar, and Gillian Kant
    In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2024
    PDF Code
  8. Interpretable Additive Tabular Transformer Networks
    Anton Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, and Benjamin Säfken
    Transactions on Machine Learning Research, 2024
    PDF Code
  9. Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion
    Anton Thielmann, Arik Reuter, Quentin Seifert, Elisabeth Bergherr, and Benjamin Säfken
    Computational Linguistics, 2024
    PDF Code
  10. Mambular: A Sequential Model for Tabular Deep Learning
    Anton Thielmann, Manish Kumar, Christoph Weisser, Arik Reuter, Benjamin Säfken, and Soheila Samiee
    arXiv preprint arXiv:2408.06291, 2024
    PDF Code
  11. GPTopic: Dynamic and Interactive Topic Representations
    Arik Reuter, Bishnu Khadka, Anton Thielmann, Christoph Weisser, Sebastian Fischer, and Benjamin Säfken
    arXiv preprint arXiv:2403.03628, 2024
    PDF Code
  12. Neural Additive Image Model: Interpretation through Interpolation
    Arik Reuter, Anton Thielmann, and Benjamin Saefken
    arXiv preprint arXiv:2405.02295, 2024
    PDF Code
  13. 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, and Benjamin Säfken
    Computational Statistics, 2023
    PDF