Arik Reuter

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.

Please feel free to contact me via email.

You can also find me on Google Scholar and GitHub.

Publications

  1. 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 (NeurIPS), 2025
    Spotlight
  2. 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 (ICML), 2025
  3. Position: The Future of Bayesian Prediction Is Prior-Fitted
    Sebastian Müller, Arik Reuter, Noah Hollmann, David Rügamer, and Frank Hutter
    In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025
  4. 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
  5. Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
    Anton Thielmann, Arik Reuter, and Benjamin Saefken
    arXiv preprint arXiv:2504.08712, 2025
  6. STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module
    Anton Thielmann, Arik Reuter, Christoph Weisser, Gillian Kant, Manish Kumar, and Benjamin Säfken
    In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2024
  7. Interpretable Additive Tabular Transformer Networks
    Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, and Benjamin Säfken
    Transactions on Machine Learning Research, 2024
  8. Topics in the haystack: Enhancing topic quality through corpus expansion
    Anton Thielmann, Arik Reuter, Quentin Seifert, Elisabeth Bergherr, and Benjamin Säfken
    Computational Linguistics, 2024
  9. Mambular: A Sequential Model for Tabular Deep Learning
    Anton Frederik Thielmann, Manish Kumar, Christoph Weisser, Arik Reuter, Benjamin Säfken, and Soheila Samiee
    arXiv preprint arXiv:2408.06291, 2024
  10. GPTopic: Dynamic and Interactive Topic Representations
    Arik Reuter, Binod Khadka, Anton Thielmann, Christoph Weisser, Sebastian Fischer, and Benjamin Säfken
    arXiv preprint arXiv:2403.03628, 2024
  11. Neural Additive Image Model: Interpretation through Interpolation
    Arik Reuter, Anton Thielmann, and Benjamin Saefken
    arXiv preprint arXiv:2405.02295, 2024
  12. 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
  13. 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 (ICML), 2025
  14. Position: The Future of Bayesian Prediction Is Prior-Fitted
    Sebastian Müller, Arik Reuter, Noah Hollmann, David Rügamer, and Frank Hutter
    In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025
  15. 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
  16. Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
    Anton Thielmann, Arik Reuter, and Benjamin Saefken
    arXiv preprint arXiv:2504.08712, 2025
  17. STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module
    Anton Thielmann, Arik Reuter, Christoph Weisser, Gillian Kant, Manish Kumar, and Benjamin Säfken
    In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2024
  18. Interpretable Additive Tabular Transformer Networks
    Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, and Benjamin Säfken
    Transactions on Machine Learning Research, 2024
  19. Topics in the haystack: Enhancing topic quality through corpus expansion
    Anton Thielmann, Arik Reuter, Quentin Seifert, Elisabeth Bergherr, and Benjamin Säfken
    Computational Linguistics, 2024
  20. Mambular: A Sequential Model for Tabular Deep Learning
    Anton Frederik Thielmann, Manish Kumar, Christoph Weisser, Arik Reuter, Benjamin Säfken, and Soheila Samiee
    arXiv preprint arXiv:2408.06291, 2024
  21. GPTopic: Dynamic and Interactive Topic Representations
    Arik Reuter, Binod Khadka, Anton Thielmann, Christoph Weisser, Sebastian Fischer, and Benjamin Säfken
    arXiv preprint arXiv:2403.03628, 2024
  22. Neural Additive Image Model: Interpretation through Interpolation
    Arik Reuter, Anton Thielmann, and Benjamin Saefken
    arXiv preprint arXiv:2405.02295, 2024
  23. 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