2 papers
cs.LG2026
Contrastive Order Learning: A General Framework for Ordinal Regression
Chaewon Lee, BeomJun Shim, Kwang Pyo Choi +1
We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. Wh…
cs.LG2026
Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
Chaewon Lee, Seon-Ho Lee, Chang-Su Kim
Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption. In this paper, we…