CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
International Conference on Learning Representations , year =
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SCARV improves global and local stability of sample rankings in redundant NLP datasets by layering robust multi-seed aggregation with structure-aware allocation over redundancy clusters.
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Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations
CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
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SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets
SCARV improves global and local stability of sample rankings in redundant NLP datasets by layering robust multi-seed aggregation with structure-aware allocation over redundancy clusters.