Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
arXiv preprint arXiv:1910.14120 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Outcome-fair credit models often exhibit hidden procedural bias through inconsistent reasoning across groups, which the CEC framework mitigates by enforcing consistent feature attributions via counterfactuals.
Length-controlled AlpacaEval applies regression adjustment to remove length bias from LLM auto-evaluations, raising Spearman correlation with Chatbot Arena from 0.94 to 0.98.
citing papers explorer
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
Outcome-fair credit models often exhibit hidden procedural bias through inconsistent reasoning across groups, which the CEC framework mitigates by enforcing consistent feature attributions via counterfactuals.
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Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Length-controlled AlpacaEval applies regression adjustment to remove length bias from LLM auto-evaluations, raising Spearman correlation with Chatbot Arena from 0.94 to 0.98.