The paper delivers the first theoretical analysis and practical zeroth-order framework for algorithmic recourse under in-context learning for tabular prediction.
Early stopping tabular in-context learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TabSwift introduces an efficient row-wise attention tabular foundation model competitive with TabPFN v2 and TabICL via gated attention stabilization and register tokens, plus adaptive layer-wise early-exit for inference.
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Algorithmic Recourse of In-Context Learning for Tabular Data
The paper delivers the first theoretical analysis and practical zeroth-order framework for algorithmic recourse under in-context learning for tabular prediction.
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TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
TabSwift introduces an efficient row-wise attention tabular foundation model competitive with TabPFN v2 and TabICL via gated attention stabilization and register tokens, plus adaptive layer-wise early-exit for inference.