| 2026 | AAAI | MultiMedBench: A Scenario-Aware Benchmark for Evaluating Knowledge Editing in Medical VQA. | Shengtao Wen, Haodong Chen, Yadong Wang, Zhongying Pan, Xiang Chen, Yu Tian, Bo Qian, Dong Liang, Sheng-Jun Huang |
| 2026 | AAAI | Reflect Then Learn: Active Prompting for Information Extraction Guided by Introspective Confusion. | Dong Zhao, Yadong Wang, Xiang Chen, Chenxi Wang, Hongliang Dai, Chuanxing Geng, Shengzhong Zhang, Shaoyuan Li, Sheng-Jun Huang |
| 2025 | AAAI | StructSR: Refuse Spurious Details in Real-World Image Super-Resolution. | Yachao Li, Dong Liang, Tianyu Ding, Sheng-Jun Huang |
| 2025 | AAAI | MLC-NC: Long-Tailed Multi-Label Image Classification Through the Lens of Neural Collapse. | Zijian Tao, Shao-Yuan Li, Wenhai Wan, Jinpeng Zheng, Jia-Yao Chen, Yuchen Li, Sheng-Jun Huang, Songcan Chen |
| 2025 | AAAI | Improving Generalization of Deep Neural Networks by Optimum Shifting. | Yuyan Zhou, Ye Li, Lei Feng, Sheng-Jun Huang |
| 2025 | ACL | Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning. | Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang |
| 2025 | CVPR | Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach. | Chen-Chen Zong, Sheng-Jun Huang |
| 2025 | ECAI | Conservative Query and Adaptive Regularization for Offline RL under Uncertainty Estimation. | Li-Rong Zhou, Qin-Wen Luo, Sheng-Jun Huang |
| 2025 | ICASSP | Learning with Partial Labels from Conflict-Free and Semi-Supervised Perspective. | Chen-Chen Zong, Sheng-Jun Huang |
| 2025 | ICML | Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL. | Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang |
| 2025 | ICML | Efficient Heterogeneity-Aware Federated Active Data Selection. | Ying-Peng Tang, Chao Ren, Xiaoli Tang, Sheng-Jun Huang, Lizhen Cui, Han Yu |
| 2025 | IJCAI | FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks. | Binbin Ding, Penghui Yang, Sheng-Jun Huang |
| 2025 | IJCAI | DM-POSA: Enhancing Open-World Test-Time Adaptation with Dual-Mode Matching and Prompt-Based Open Set Adaptation. | Shiji Zhao, Shao-Yuan Li, Chuanxing Geng, Sheng-Jun Huang, Songcan Chen |
| 2025 | IJCAI | Inconsistency-Based Federated Active Learning. | Chen-Chen Zong, Tong Jin, Sheng-Jun Huang |
| 2025 | IJCNN | Data-efficient LLM Fine-tuning for Code Generation. | Weijie Lv, Xuan Xia, Sheng-Jun Huang |
| 2025 | KDD | Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head. | Penghui Yang, Chen-Chen Zong, Sheng-Jun Huang, Lei Feng, Bo An |
| 2024 | AAAI | Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. | Wenhai Wan, Xinrui Wang, Ming-Kun Xie, Shao-Yuan Li, Sheng-Jun Huang, Songcan Chen |
| 2024 | AAAI | Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. | Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang |
| 2024 | ECCV | Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. | Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2024 | ECCV | Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation. | Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning, Haibo Ye, Sheng-Jun Huang |
| 2024 | ICLR | One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models. | Sheng-Jun Huang, Yi Li, Yiming Sun, Ying-Peng Tang |
| 2024 | ICML | Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. | Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2024 | IJCAI | Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations. | Ye Li, Siqi Chen, Bin Shan, Sheng-Jun Huang |
| 2024 | IJCAI | NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes. | Shiji Zhao, Shao-Yuan Li, Sheng-Jun Huang |
| 2024 | KDD | Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning. | Hao-Zhe Liu, Ming-Kun Xie, Chen-Chen Zong, Sheng-Jun Huang |
| 2023 | AAAI | Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks. | Ye Li, Songcan Chen, Sheng-Jun Huang |
| 2023 | ICCV | Multi-Label Knowledge Distillation. | Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2023 | ICCV | Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery. | Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li |
| 2023 | IJCAI | ALL-E: Aesthetics-guided Low-light Image Enhancement. | Ling Li, Dong Liang, Yuanhang Gao, Sheng-Jun Huang, Songcan Chen |
| 2022 | CVPR | Active Learning for Open-set Annotation. | Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang |
| 2021 | AAAI | Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries. | Kun-Peng Ning, Lue Tao, Songcan Chen, Sheng-Jun Huang |
| 2021 | IJCAI | Asynchronous Active Learning with Distributed Label Querying. | Sheng-Jun Huang, Chen-Chen Zong, Kun-Peng Ning, Haibo Ye |
| 2021 | IJCAI | Dual Active Learning for Both Model and Data Selection. | Ying-Peng Tang, Sheng-Jun Huang |
| 2021 | KDD | Partial Multi-Label Learning with Meta Disambiguation. | Ming-Kun Xie, Feng Sun, Sheng-Jun Huang |
| 2020 | AAAI | Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning. | Zhao-Yang Liu, Shaoyuan Li, Songcan Chen, Yao Hu, Sheng-Jun Huang |
| 2020 | AAAI | Partial Multi-Label Learning with Noisy Label Identification. | Ming-Kun Xie, Sheng-Jun Huang |
| 2020 | AAAI | Active Learning with Query Generation for Cost-Effective Text Classification. | Yifan Yan, Sheng-Jun Huang, Shaoyi Chen, Meng Liao, Jin Xu |
| 2020 | ICDM | Semi-Supervised Partial Multi-Label Learning. | Ming-Kun Xie, Sheng-Jun Huang |
| 2020 | ICML | Cost-effectively Identifying Causal Effects When Only Response Variable is Observable. | Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou |
| 2019 | AAAI | Active Sampling for Open-Set Classification without Initial Annotation. | Zhao-Yang Liu, Sheng-Jun Huang |
| 2019 | AAAI | Self-Paced Active Learning: Query the Right Thing at the Right Time. | Ying-Peng Tang, Sheng-Jun Huang |
| 2019 | IJCAI | Multi-View Active Learning for Video Recommendation. | Jia-Jia Cai, Jun Tang, Qing-Guo Chen, Yao Hu, Xiaobo Wang, Sheng-Jun Huang |
| 2019 | KDD | Learning Class-Conditional GANs with Active Sampling. | Ming-Kun Xie, Sheng-Jun Huang |
| 2019 | SDM | Towards Identifying Causal Relation Between Instances and Labels. | Tian-Zuo Wang, Sheng-Jun Huang, Zhi-Hua Zhou |
| 2018 | AAAI | Dual Set Multi-Label Learning. | Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou |
| 2018 | AAAI | Partial Multi-Label Learning. | Ming-Kun Xie, Sheng-Jun Huang |
| 2018 | IJCAI | Cost-Effective Active Learning for Hierarchical Multi-Label Classification. | Yifan Yan, Sheng-Jun Huang |
| 2018 | KDD | Active Feature Acquisition with Supervised Matrix Completion. | Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen |
| 2018 | KDD | Cost-Effective Training of Deep CNNs with Active Model Adaptation. | Sheng-Jun Huang, Jia-Wei Zhao, Zhao-Yang Liu |
| 2017 | IJCAI | Cost-Effective Active Learning from Diverse Labelers. | Sheng-Jun Huang, Jia-Lve Chen, Xin Mu, Zhi-Hua Zhou |
| 2017 | IJCAI | Multi-instance multi-label active learning. | Sheng-Jun Huang, Nengneng Gao, Songcan Chen |
| 2017 | SDM | Margin Distribution Logistic Machine. | Yi Ding, Sheng-Jun Huang, Chen Zu, Daoqiang Zhang |
| 2016 | IJCAI | Transfer Learning with Active Queries from Source Domain. | Sheng-Jun Huang, Songcan Chen |
| 2015 | IJCAI | Multi-Label Active Learning: Query Type Matters. | Sheng-Jun Huang, Songcan Chen, Zhi-Hua Zhou |
| 2014 | AAAI | Fast Multi-Instance Multi-Label Learning. | Sheng-Jun Huang, Wei Gao, Zhi-Hua Zhou |
| 2013 | ICDM | Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning. | Sheng-Jun Huang, Zhi-Hua Zhou |
| 2012 | AAAI | Multi-Label Learning by Exploiting Label Correlations Locally. | Sheng-Jun Huang, Zhi-Hua Zhou |
| 2012 | KDD | Multi-label hypothesis reuse. | Sheng-Jun Huang, Yang Yu, Zhi-Hua Zhou |