We are glad to announce that two order learning papers (ConOrd and SOL) will be presented at ICML 2026 in Seoul, Korea this July. Starting from the world-first order learning paper at ICLR 2020, our lab has been developing a series of order learning techniques over the past six years.
- 1. Chaewon Lee, BeomJun Shim, Kwang Pyo Choi, and Chang-Su Kim, “Contrastive order learning: A general framework for ordinal regression,” ICML 2026. (ConOrd)
- 2. Chaewon Lee, Seon-Ho Lee, and Chang-Su Kim, “Stochastic order learning: an approach to rank estimation using noisy data,” ICML 2026. (SOL)
- 3. Nyeong-Ho Shin, Seon-Ho Lee, and Chang-Su Kim, “Blind image quality assessment based on geometric order learning,” CVPR 2024. (GOL-based BIQA)
- 4. Seon-Ho Lee, Nyeong-Ho Shin, and Chang-Su Kim, “Unsupervised order learning,” ICLR 2024. (UOL)
- 5. Seon-Ho Lee, Nyeong Ho Shin, and Chang-Su Kim, “Geometric order learning for rank estimation,” NeurIPS 2022. (GOL)
- 6. Nyeong Ho Shin, Seon-Ho Lee, and Chang-Su Kim, “Moving window regression: A novel approach to ordinal regression,” CVPR 2022. (MWR)
- 7. Seon-Ho Lee and Chang-Su Kim, “Order learning using partially ordered data via chainization,” ECCV 2022. (Chainization)
- 8. Seon-Ho Lee and Chang-Su Kim, “Deep repulsive clustering of ordered data based on order-identity decomposition,” ICLR 2021. (DRC-ORID)
- 9. Kyungsun Lim, Nyeong-Ho Shin, Young-Yoon Lee, and Chang-Su Kim, “Order learning and its application to age estimation,” ICLR 2020. (OL)
Congratulations to Chaewon and all the authors! Following MCL tradition, the ConOrd paper might have been named COL — but Chaewon apparently had no intention of following it, and named it ConOrd instead. We think it sounds great anyway. Next up: VOL!
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