Anchor PCA recovers a maximal invariant subspace for multi-domain data via PCA on a modified target matrix that trades off explained variance with domain agreement.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =
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Perturbing prefixes to semantic neighbors during training creates a hierarchical noise model that improves language model predictions on token sequences outside the training corpus support.
Representation Curriculum stages feature introduction during training to prioritize content merit signals over exposure-dependent ones, reducing shortcut learning and improving cold-start robustness in ranking.
citing papers explorer
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Anchor PCA
Anchor PCA recovers a maximal invariant subspace for multi-domain data via PCA on a modified target matrix that trades off explained variance with domain agreement.
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Perturbation is All You Need for Extrapolating Language Models
Perturbing prefixes to semantic neighbors during training creates a hierarchical noise model that improves language model predictions on token sequences outside the training corpus support.
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Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Representation Curriculum stages feature introduction during training to prioritize content merit signals over exposure-dependent ones, reducing shortcut learning and improving cold-start robustness in ranking.