MIND decouples high-dimensional model-induced label noise into subspace components via latent manifold disentanglement and a Latent Decoupling Estimator.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
CARE is a parameter-efficient framework that aggregates predictions from noisy labels, VLM text embeddings, and visual features with class-frequency-based agreement thresholds to rectify labels in long-tailed noisy datasets.
citing papers explorer
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MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement
MIND decouples high-dimensional model-induced label noise into subspace components via latent manifold disentanglement and a Latent Decoupling Estimator.
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Optimal Transport for LLM Reward Modeling from Noisy Preference
SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
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CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
CARE is a parameter-efficient framework that aggregates predictions from noisy labels, VLM text embeddings, and visual features with class-frequency-based agreement thresholds to rectify labels in long-tailed noisy datasets.