TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
IEEE Transactions on Information Theory , volume=
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Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.
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Learning Causal Orderings for In-Context Tabular Prediction
TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.