Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data. - Yingbin Bai; Tongliang Liu (ICCV, 2021)

Understanding and Improving Early Stopping for Learning with Noisy Labels. - Yingbin Bai; Erkun Yang; Bo Han 003; Yanhua Yang; Jiatong Li; Yinian Mao; Gang Niu 001; Tongliang Liu (NeurIPS, 2021)

RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning. - Yingbin Bai; Erkun Yang; Zhaoqing Wang; Yuxuan Du; Bo Han 003; Cheng Deng; Dadong Wang; Tongliang Liu (NeurIPS, 2022)

MSR: Making Self-supervised learning Robust to Aggressive Augmentations. - Yingbin Bai; Erkun Yang; Zhaoqing Wang; Yuxuan Du; Bo Han 003; Cheng Deng; Dadong Wang; Tongliang Liu (CoRR, 2022)

Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. - Yingbin Bai; Zhongyi Han; Erkun Yang; Jun Yu 001; Bo Han 003; Dadong Wang; Tongliang Liu (NeurIPS, 2023)

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge. - Hongwei Bran Li; Fernando Navarro; Ivan Ezhov; Amirhossein Bayat; Dhritiman Das; Florian Kofler; Suprosanna Shit; Diana Waldmannstetter; Johannes C. Paetzold; Xiaobin Hu; Benedikt Wiestler; Lucas Zimmer; et al. (CoRR, 2024)

Learning Efficiency Meets Symmetry Breaking. - Yingbin Bai; Sylvie Thiébaux; Felipe W. Trevizan (CoRR, 2025)