L2C2 is a deep RL framework that learns to clean tabular data by aligning it to the synthetic prior of tabular foundation models, yielding higher accuracy on some benchmarks and cross-dataset policy transfer.
arXiv preprint arXiv:1904.09483 75 (2019)
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ESP measures model sensitivity to feature errors and shows performance drops are not always predictable from simple target correlations.
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Prior-Aligned Data Cleaning for Tabular Foundation Models
L2C2 is a deep RL framework that learns to clean tabular data by aligning it to the synthetic prior of tabular foundation models, yielding higher accuracy on some benchmarks and cross-dataset policy transfer.
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Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile
ESP measures model sensitivity to feature errors and shows performance drops are not always predictable from simple target correlations.