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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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Establishes component-wise identifiability guarantees for partially shared causal latents in multimodal nonlinear mixing and introduces a differentiable Wasserstein-based module for recovery.
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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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Identifiable Multimodal Causal Representation Learning under Partial Latent Sharing
Establishes component-wise identifiability guarantees for partially shared causal latents in multimodal nonlinear mixing and introduces a differentiable Wasserstein-based module for recovery.