Standard DPO surrogates are inconsistent for equicontinuous neural nets; SA-DPO provides structure-aware H-consistency bounds by adapting margins to semantic distance and shows heavy-tailed losses yield superior guarantees for capacity-bounded models via the Margin-Capacity Profile.
Balancing the scales: A theoretical and algorithmic framework for learning from imbalanced data
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MILD reformulates two-stage learning to defer as cost-sensitive learning over the input-expert domain and derives new margin-based losses with guarantees, yielding better performance than baselines on image classification and LLM routing tasks.
citing papers explorer
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Mind the Gap: Structure-Aware Consistency in Preference Learning
Standard DPO surrogates are inconsistent for equicontinuous neural nets; SA-DPO provides structure-aware H-consistency bounds by adapting margins to semantic distance and shows heavy-tailed losses yield superior guarantees for capacity-bounded models via the Margin-Capacity Profile.
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Optimized Deferral for Imbalanced Settings
MILD reformulates two-stage learning to defer as cost-sensitive learning over the input-expert domain and derives new margin-based losses with guarantees, yielding better performance than baselines on image classification and LLM routing tasks.