About me

I am a Ph.D. candidate (since 2019) in the School of Sofware at Shandong University, advised by Prof. Yilong Yin and Zhongyi Han. In addition, I have done research at Westlake University with Prof. Tailin Wu in 2023.09-2024.02. I plan to graduate in June 2024 and do postdoctoral work after that.

Research Direction：

Machine Learning; Semi-supervised Learning; Out-of-distribution Detection; Open-world Learning; AI for Science; Graph learning

Education :

2019.09-present: Ph.D. candidate in Shandong University (Jinan).

2015.09-2019.06: B.E. in Shandong University (Jinan).

Services [Program committee or reviewer for conferences] :

• TNNLS
• IJCAI 2024
• Neural Networks
• ICLR 2024
• TKDE
• CICAI 2023
• ACM MM 2023
• NeurIPS 2023-2024
• ICML 2023-2024
• CVPR 2023
• AAAI 2023-2024

Recent News

• 2024/05/31: 1 paper accepted to IJCV.
• 2024/03/05: 1 paper accepted to ICLR 2024 Workshop ME-FoMo.
• 2024/02/27: 1 paper accepted to CVPR 2024.
• 2023/12/09: 1 paper accepted to AAAI 2024.
• 2023/09/11: 1 paper accepted to Machine Learning Journal.
• 2023/07/26: 2 papers accepted to ACMMM 2023.
• 2023/05/17: 1 paper accepted to TKDE.
• 2023/05/12: 1 paper accepted to PR.
• 2023/02/08: 1 paper accepted to CVPR 2023.
• 2022/11/19: 1 paper accepted to AAAI 2023.
• 2022/11/17: Ph.D. President’s Scholarship (2021-2022)
• 2022/10/19: Ph.D. National Scholarship (2021-2022).
• 2022/09/17: 1 paper accepted to ACML 2022.
• 2022/06/30: 1 paper (oral) accepted to ACMMM 2022.
• 2022/05/20: 1 paper accepted to KBS.
• 2022/03/03: 1 papers accepted to CVPR 2022.
• 2021/12/01: 1 paper (oral) accepted to AAAI 2022.
• 2021/05/31: 1 paper accepted to CICAI 2021.
• 2020/12/02: 1 paper accepted to IPMI 2021.
• 2020/11/23: 1 paper accepted to TCSVT.

Publications

Preprint

• Hao Sun, Rundong He*, Zhongyi Han, Zhicong Lin, Yongshun Gong, Yilong Yin. (Note: * denotes the corresponding author)

  CLIP-driven Outliers Synthesis for few-shot OOD detection. paper

• Qikai Wang#, Rundong He#, Yongshun Gong, Chunxiao Ren, Haoliang Sun, Xiaoshui Huang, Yilong Yin. (Note: # denotes the co-first author)

  Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch. paper

Accepted Conference

• Yue Yuan, Rundong He*, Yicong Dong, Zhongyi Han, Yilong Yin. (Note: * denotes the corresponding author)

  Discriminability-Driven Channel Selection for Out-of-Distribution Detection. CVPR 2024 (CCF A) code

• Rundong He; Yue Yuan; Zhongyi Han; Fan Wang; Wan Su; Yilong Yin; Tongliang Liu; Yongshun Gong.

  Exploring Channel-Aware Typical Features for Out-of-Distribution Detection. AAAI 2024 (CCF A) code

• Rundong He; Rongxue Li; Zhongyi Han; Yilong Yin.

  Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection. MM 2023 (CCF A)

• Yue Yuan; Rundong He; Zhongyi Han; Yilong Yin.

  LHAct: Rectifying Extremely Low and High Activations for Out-of-Distribution Detection. MM 2023 (CCF A) code

• Rundong He; Zhongyi Han; Xiankai Lu; Yilong Yin.

  RONF: Reliable Outlier Synthesis under Noisy Feature Space for Out-of-Distribution Detection. MM 2022 (CCF A, oral)

• Rundong He; Zhongyi Han; Xiankai Lu; Yilong Yin.

  Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data. CVPR 2022 (CCF A). code

• Rundong He; Zhongyi Han; Yang Yang; Yilong Yin.

  Not All Parameters Should be Treated Equally: Deep Safe Semi-Supervised Learning under Class Distribution Mismatch. AAAI 2022 (CCF A, oral). code

• Rundong He, Zhongyi Han, Yu Zhang, Xueying He, Xiushan Nie, Yilong Yin.

  Robust Anomaly Detection from Partially Observed Anomalies with Augmented Classes. CICAI 2021 paper

• Fan Wang; Zhongyi Han; Zhiyan Zhang; Rundong He; Yilong Yin

  MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation. CVPR 2023 (CCF A).

• Zhongyi Han; Zhiyan Zhang; Fan Wang; Rundong He; Wan Su; Yilong Yin

  Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation. AAAI 2023 (CCF A).

• Zhongyi Han; Wan Su; Rundong He; Yilong Yin

  SNAIL: Semi-Separated Uncertainty Adversarial Learning for Universal Domain Adaptation. ACML 2022

• Zhongyi Han, Rundong He, Tianyang Li, Benzheng Wei, Jian Wang, Yilong Yin.

  Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator. (IPMI 2021, 医学顶会). paper

Accepted Journal

• Rundong He, Zhongyi Han, Xiushan Nie, Yilong Yin, Xiaojun Chang.

  Visual Out-of-Distribution Detection in Open-Set Noisy Environments. International Journal of Computer Vision (IJCV) (CCF A), 2024.

• Rundong He; Zhongyi Han; Xiankai Lu; Yilong Yin.

  SAFER-STUDENT for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data. TKDE (CCF A), 2023.

• Rundong He; Zhongyi Han; Yilong Yin.

  Towards Safe and Robust Weakly-Supervised Anomaly Detection under Subpopulation Shift. KBS (JCR Q1), 2022.

• Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Rundong He, Yilong Yin.

  Learning Sample-Aware Confidence Threshold for Semi-Supervised Learning. Machine Learning Journal (MLJ) (CCF B), 2023.

• Wan Su; Zhongyi Han; Rundong He; Benzheng Wei, Xueying He, and Yilong Yin.

  Neighborhood-based Credibility Anchor Learning for Universal Domain Adaptation. PR (JCR Q1), 2023

• Yu Zhang, Xiushan Nie, Rundong He, Meng Chen, Yilong Yin.

  Normality Learning in Multispace for Video Anomaly Detection. TCSVT (JCR Q1), 2020. paper