2025
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Robertson, Jake; Reuter, Arik; Guo, Siyuan; Hollmann, Noah; Hutter, Frank; Schölkopf, Bernhard Do-PFN: In-Context Learning for Causal Effect Estimation Inproceedings Forthcoming In: 39th Conference on Neural Information Processing Systems (NeurIPS), Forthcoming, (Spotlight). @inproceedings{Robertson2025b,
title = {Do-PFN: In-Context Learning for Causal Effect Estimation},
author = {Jake Robertson and Arik Reuter and Siyuan Guo and Noah Hollmann and Frank Hutter and Bernhard Schölkopf},
year = {2025},
booktitle = {39th Conference on Neural Information Processing Systems (NeurIPS)},
keywords = {}
}
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Grinsztajn, Leo; Flöge, Klemens; Key, Oscar; Hayler, Adrian; Manium, Mihir; Garg, Anurag; Robertson, Jake; Hoo, Shi Bin; Birkel, Felix; Jund, Philipp; Jäger, Benjamin; Yu, Rosen Ting-Ying; Schölkopf, Bernhard; Hollmann, Noah; Hutter, Frank TabPFN-2.5: a Preview Inproceedings Forthcoming In: EurIPS 2025 Workshop: AI for Tabular Data, Forthcoming. @inproceedings{Grinsztajn25,
title = {TabPFN-2.5: a Preview},
author = {Leo Grinsztajn and Klemens Flöge and Oscar Key and Adrian Hayler and Mihir Manium and Anurag Garg and Jake Robertson and Shi Bin Hoo and Felix Birkel and Philipp Jund and Benjamin Jäger and Rosen Ting-Ying Yu and Bernhard Schölkopf and Noah Hollmann and Frank Hutter},
year = {2025},
booktitle = {EurIPS 2025 Workshop: AI for Tabular Data},
keywords = {}
}
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Swelam, Omar; Purucker, Lennart; Robertson, Jake; Raum, Hanne; Boedecker, Joschka; Hutter, Frank Does TabPFN Understand Causal Structures? Inproceedings Forthcoming In: EurIPS 2025 Workshop: AI for Tabular Data, Forthcoming. @inproceedings{Swelam25,
title = {Does TabPFN Understand Causal Structures?},
author = {Omar Swelam and Lennart Purucker and Jake Robertson and Hanne Raum and Joschka Boedecker and Frank Hutter},
year = {2025},
booktitle = {EurIPS 2025 Workshop: AI for Tabular Data},
keywords = {}
}
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Robertson, Jake; Reuter, Arik; Guo, Siyuan; Hollmann, Noah; Hutter, Frank; Schölkopf, Bernhard Do-PFN: In-Context Learning for Causal Effect Estimation Inproceedings In: Foundation Models for Structured Data workshop at ICML, 2025. @inproceedings{nokey,
title = {Do-PFN: In-Context Learning for Causal Effect Estimation},
author = {Jake Robertson and Arik Reuter and Siyuan Guo and Noah Hollmann and Frank Hutter and Bernhard Schölkopf},
year = {2025},
booktitle = {Foundation Models for Structured Data workshop at ICML},
keywords = {}
}
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Robertson, Jake; Hollmann, Noah; Müller, Samuel Gabriel; Awad, Noor; Hutter, Frank FairPFN: A Tabular Foundation Model for Causal Fairness Inproceedings In: Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025. @inproceedings{Robertson2025,
title = {FairPFN: A Tabular Foundation Model for Causal Fairness},
author = {Jake Robertson and Noah Hollmann and Samuel Gabriel Müller and Noor Awad and Frank Hutter},
year = {2025},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
keywords = {}
}
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2024
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Robertson, Jake; Schmidt, Thorsten; Hutter, Frank; Awad, Noor A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes Inproceedings In: Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24), 2024. @inproceedings{Robertson2024b,
title = {A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes},
author = { Jake Robertson and Thorsten Schmidt and Frank Hutter and Noor Awad},
year = {2024},
booktitle = {Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24)},
keywords = {}
}
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Robertson, Jake; Hollmann, Noah; Awad, Noor; Hutter, Frank FairPFN: Transformers Can do Counterfactual Fairness Conference Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track, 2024. @conference{Robertson2024,
title = {FairPFN: Transformers Can do Counterfactual Fairness},
author = {Jake Robertson and Noah Hollmann and Noor Awad and Frank Hutter},
year = {2024},
booktitle = {Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track},
keywords = {}
}
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