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. 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. 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. 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. 4. Seon-Ho Lee, Nyeong-Ho Shin, and Chang-Su Kim, “Unsupervised order learning,” ICLR 2024. (UOL)
5. 5. Seon-Ho Lee, Nyeong Ho Shin, and Chang-Su Kim, “Geometric order learning for rank estimation,” NeurIPS 2022. (GOL)
6. 6. Nyeong Ho Shin, Seon-Ho Lee, and Chang-Su Kim, “Moving window regression: A novel approach to ordinal regression,” CVPR 2022. (MWR)
7. 7. Seon-Ho Lee and Chang-Su Kim, “Order learning using partially ordered data via chainization,” ECCV 2022. (Chainization)
8. 8. Seon-Ho Lee and Chang-Su Kim, “Deep repulsive clustering of ordered data based on order-identity decomposition,” ICLR 2021. (DRC-ORID)
9. 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!