Stochastic Order Learning associates each instance with multiple plausible ranks and trains embeddings via complementary discriminative and stochastic-order losses that remain robust to ordinal label noise.
ICLR , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.LG 2years
2026 2representative 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.
citing papers explorer
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Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
Stochastic Order Learning associates each instance with multiple plausible ranks and trains embeddings via complementary discriminative and stochastic-order losses that remain robust to ordinal label noise.
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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.