MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
Do-PFN: In-Context Learning for Causal Effect Estimation
3 Pith papers cite this work. Polarity classification is still indexing.
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A prior-data fitted network amortizes causal sensitivity analysis by generating training labels via Lagrangian scalarization, achieving orders-of-magnitude faster bounds computation than per-instance methods.
TabDistill distills feature interactions from tabular foundation models via post-hoc attribution and inserts them into GAMs, yielding consistent predictive gains.
citing papers explorer
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MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
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Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
A prior-data fitted network amortizes causal sensitivity analysis by generating training labels via Lagrangian scalarization, achieving orders-of-magnitude faster bounds computation than per-instance methods.
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Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models
TabDistill distills feature interactions from tabular foundation models via post-hoc attribution and inserts them into GAMs, yielding consistent predictive gains.