TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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SCOPE implements a relaxed sparsest-permutation approach for scalable causal structure learning that recovers Markov equivalence classes up to 10k variables using incomplete Cholesky factorization on screened supports.
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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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Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
SCOPE implements a relaxed sparsest-permutation approach for scalable causal structure learning that recovers Markov equivalence classes up to 10k variables using incomplete Cholesky factorization on screened supports.