Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane. - Hao Cheng 005; Dongze Lian; Shenghua Gao; Yanlin Geng (ECCV (11), 2018)

Local to Global Learning: Gradually Adding Classes for Training Deep Neural Networks. - Hao Cheng 005; Dongze Lian; Bowen Deng; Shenghua Gao; Tao Tan; Yanlin Geng (CVPR, 2019)

Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification. - Hao Cheng 005; Dongze Lian; Shenghua Gao; Yanlin Geng (Entropy, 2019)

Demystifying How Self-Supervised Features Improve Training from Noisy Labels. - Hao Cheng 005; Zhaowei Zhu; Xing Sun 001; Yang Liu 018 (CoRR, 2021)

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. - Jiaheng Wei; Zhaowei Zhu; Hao Cheng 005; Tongliang Liu; Gang Niu 001; Yang Liu 018 (ICLR, 2022)

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies. - Hao Cheng 005; Qingsong Wen; Yang Liu 018; Liang Sun 001 (ICLR, 2024)

Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels. - Yaxuan Wang; Hao Cheng 005; Jing Xiong; Qingsong Wen; Han Jia; Ruixuan Song; Liyuan Zhang; Zhaowei Zhu; Yang Liu 018 (KDD (1), 2025)