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Recent foundational models for tabular data, such as TabPFN, excel at adapting to new tasks via in-context learning, but remain constrained to a fixed, pre-defined number of target dimensions-often necessitating costly ensembling strategies.
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Why do tree-based models still outperform deep learning on typical tabular data?
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L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P. Xing, H. Zhang, J. E. Gonzalez, and I. Stoica · 2023
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Forecastpfn: Synthetically-trained zero-shot forecasting
S. Dooley, G. S. Khurana, C. Mohapatra, S. V. Naidu, and C. White · 2024
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TabMDA: Tabular manifold data augmentation for any classifier using transformers with in-context subsetting
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N. Hollmann, S. Müller, K. Eggensperger, and F. Hutter · 2023
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When do neural nets outperform boosted trees on tabular data?
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Mothernet: A foundational hypernetwork for tabular classification
A. Müller, C. Curino, and R. Ramakrishnan · 2023
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PFNs4BO: In-context learning for Bayesian optimization
S. Müller, M. Feurer, N. Hollmann, and F. Hutter · 2023
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Statistical foundations of prior-data fitted networks
T. Nagler · 2023
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J. Robertson, N. Hollmann, N. Awad, and F. Hutter · 2024
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Revisiting nearest neighbor for tabular data: A deep tabular baseline two decades later
H.-J. Ye, H.-H. Yin, D.-C. Zhan, and W.-L. Chao · 2024
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Set-llm: A permutation-invariant llm, 2025
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