MIML: A Framework for Learning with Ambiguous Objects - Zhi-Hua Zhou; Min-Ling Zhang; Sheng-Jun Huang; Yufeng Li 008 (CoRR, 2008)

Multi-Label Learning by Exploiting Label Correlations Locally. - Sheng-Jun Huang; Zhi-Hua Zhou (AAAI, 2012)

Multi-label hypothesis reuse. - Sheng-Jun Huang; Yang Yu 001; Zhi-Hua Zhou (KDD, 2012)

Multi-instance multi-label learning. - Zhi-Hua Zhou; Min-Ling Zhang; Sheng-Jun Huang; Yufeng Li 008 (Artif. Intell., 2012)

Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning. - Sheng-Jun Huang; Zhi-Hua Zhou (ICDM, 2013)

Fast Multi-Instance Multi-Label Learning. - Sheng-Jun Huang; Zhi-Hua Zhou (CoRR, 2013)

Active Learning by Querying Informative and Representative Examples. - Sheng-Jun Huang; Rong Jin 001; Zhi-Hua Zhou (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning. - Jian-Sheng Wu; Sheng-Jun Huang; Zhi-Hua Zhou (IEEE ACM Trans. Comput. Biol. Bioinform., 2014)

Multi-Label Active Learning: Query Type Matters. - Sheng-Jun Huang; Songcan Chen; Zhi-Hua Zhou (IJCAI, 2015)

Transfer Learning with Active Queries from Source Domain. - Sheng-Jun Huang; Songcan Chen (IJCAI, 2016)

Multi-label active learning by model guided distribution matching. - Nengneng Gao; Sheng-Jun Huang; Songcan Chen (Frontiers Comput. Sci., 2016)

Cost-Effective Active Learning from Diverse Labelers. - Sheng-Jun Huang; Jia-Lve Chen; Xin Mu; Zhi-Hua Zhou (IJCAI, 2017)

Multi-instance multi-label active learning. - Sheng-Jun Huang; Nengneng Gao; Songcan Chen (IJCAI, 2017)

Margin Distribution Logistic Machine. - Yi Ding; Sheng-Jun Huang; Chen Zu; Daoqiang Zhang (SDM, 2017)

Dual Set Multi-Label Learning. - Chong Liu 007; Peng Zhao 006; Sheng-Jun Huang; Yuan Jiang 001; Zhi-Hua Zhou (AAAI, 2018)

Partial Multi-Label Learning. - Ming-Kun Xie; Sheng-Jun Huang (AAAI, 2018)

Cost-Effective Active Learning for Hierarchical Multi-Label Classification. - Yifan Yan; Sheng-Jun Huang (IJCAI, 2018)

Active Feature Acquisition with Supervised Matrix Completion. - Sheng-Jun Huang; Miao Xu 001; Ming-Kun Xie; Masashi Sugiyama; Gang Niu 001; Songcan Chen (KDD, 2018)

Cost-Effective Training of Deep CNNs with Active Model Adaptation. - Sheng-Jun Huang; Jia-Wei Zhao; Zhao-Yang Liu (KDD, 2018)

Cross modal similarity learning with active queries. - Nengneng Gao; Sheng-Jun Huang; Yifan Yan; Songcan Chen (Pattern Recognit., 2018)

WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory. - Muhammad Yousefnezhad; Sheng-Jun Huang; Daoqiang Zhang (IEEE Trans. Cybern., 2018)

Joint Estimation of Multiple Conditional Gaussian Graphical Models. - Feihu Huang 001; Songcan Chen; Sheng-Jun Huang (IEEE Trans. Neural Networks Learn. Syst., 2018)

Active Sampling for Open-Set Classification without Initial Annotation. - Zhao-Yang Liu; Sheng-Jun Huang (AAAI, 2019)

Self-Paced Active Learning: Query the Right Thing at the Right Time. - Ying-Peng Tang; Sheng-Jun Huang (AAAI, 2019)

Multi-View Active Learning for Video Recommendation. - Jia-Jia Cai; Jun Tang; Qing-Guo Chen; Yao Hu; Xiaobo Wang; Sheng-Jun Huang (IJCAI, 2019)

Learning Class-Conditional GANs with Active Sampling. - Ming-Kun Xie; Sheng-Jun Huang (KDD, 2019)

Towards Identifying Causal Relation Between Instances and Labels. - Tian-Zuo Wang; Sheng-Jun Huang; Zhi-Hua Zhou (SDM, 2019)

ALiPy: Active Learning in Python. - Ying-Peng Tang; Guo-Xiang Li; Sheng-Jun Huang (CoRR, 2019)

Fast Multi-Instance Multi-Label Learning. - Sheng-Jun Huang; Wei Gao 008; Zhi-Hua Zhou (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning. - Zhao-Yang Liu; Shaoyuan Li; Songcan Chen; Yao Hu; Sheng-Jun Huang (AAAI, 2020)

Active Learning with Query Generation for Cost-Effective Text Classification. - Yifan Yan; Sheng-Jun Huang; Shaoyi Chen; Meng Liao; Jin Xu 014 (AAAI, 2020)

Cross-Task and Cross-Model Active Learning with Meta Features. - Guo-Xiang Li; Yao-Feng Tu; Sheng-Jun Huang (ICBDS, 2020)

Semi-Supervised Partial Multi-Label Learning. - Ming-Kun Xie; Sheng-Jun Huang (ICDM, 2020)

Cost-effectively Identifying Causal Effects When Only Response Variable is Observable. - Tian-Zuo Wang; Xi-Zhu Wu; Sheng-Jun Huang; Zhi-Hua Zhou (ICML, 2020)

Reinforcement Learning with Supervision from Noisy Demonstrations. - Kun-Peng Ning; Sheng-Jun Huang (CoRR, 2020)

LGSLRR: Towards fusing discriminative ordinal local and global structured low-rank representation for image recognition. - Qi Zhu 001; Rui Zhang; Sheng-Jun Huang; Zheng Zhang 006; Daoqiang Zhang (Inf. Sci., 2020)

Incremental Multi-Label Learning with Active Queries. - Sheng-Jun Huang; Guo-Xiang Li; Wen-Yu Huang; Shao-Yuan Li (J. Comput. Sci. Technol., 2020)

Adaptive feature weighting for robust Lp-norm sparse representation with application to biometric image classification. - Qi Zhu 001; Nuoya Xu; Sheng-Jun Huang; Jianjun Qian; Daoqiang Zhang (Int. J. Mach. Learn. Cybern., 2020)

Latent correlation embedded discriminative multi-modal data fusion. - Qi Zhu 001; Xiangyu Xu 003; Ning Yuan; Zheng Zhang 006; Donghai Guan; Sheng-Jun Huang; Daoqiang Zhang (Signal Process., 2020)

Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages. - Wei Shao 005; Sheng-Jun Huang; Mingxia Liu 001; Daoqiang Zhang (IEEE ACM Trans. Comput. Biol. Bioinform., 2020)

Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries. - Kun-Peng Ning; Lue Tao; Songcan Chen; Sheng-Jun Huang (AAAI, 2021)

Weakly Supervised Crowdsourcing Learning Based on Adversarial Consensus. - Meng-Long Wei; Shao-Yuan Li; Sheng-Jun Huang (CSCI, 2021)

Asynchronous Active Learning with Distributed Label Querying. - Sheng-Jun Huang; Chen-Chen Zong; Kun-Peng Ning; Haibo Ye (IJCAI, 2021)

Dual Active Learning for Both Model and Data Selection. - Ying-Peng Tang; Sheng-Jun Huang (IJCAI, 2021)

Partial Multi-Label Learning with Meta Disambiguation. - Ming-Kun Xie; Feng Sun; Sheng-Jun Huang (KDD, 2021)

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training. - Lue Tao; Lei Feng 006; Jinfeng Yi; Sheng-Jun Huang; Songcan Chen (NeurIPS, 2021)

Multi-Label Learning with Pairwise Relevance Ordering. - Ming-Kun Xie; Sheng-Jun Huang (NeurIPS, 2021)

Crowdsourcing aggregation with deep Bayesian learning. - Shaoyuan Li; Sheng-Jun Huang; Songcan Chen (Sci. China Inf. Sci., 2021)

Provable Defense Against Delusive Poisoning. - Lue Tao; Lei Feng 006; Jinfeng Yi; Sheng-Jun Huang; Songcan Chen (CoRR, 2021)

Co-Imitation Learning without Expert Demonstration. - Kun-Peng Ning; Hu Xu; Kun Zhu 001; Sheng-Jun Huang (CoRR, 2021)

Preface. - Min-Ling Zhang; Sheng-Jun Huang; Mingsheng Long (J. Comput. Sci. Technol., 2021)

Visual-guided attentive attributes embedding for zero-shot learning. - Rui Zhang; Qi Zhu 001; Xiangyu Xu 003; Daoqiang Zhang; Sheng-Jun Huang (Neural Networks, 2021)

PU Active Learning for Recommender Systems. - Jia-Lue Chen; Jia-Jia Cai; Yuan Jiang 001; Sheng-Jun Huang (Neural Process. Lett., 2021)

Recent Advances in Open Set Recognition: A Survey. - Chuanxing Geng; Sheng-Jun Huang; Songcan Chen (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Label Distribution Learning with Label Correlations on Local Samples. - Xiuyi Jia; Zechao Li; Xiang Zheng; Weiwei Li 001; Sheng-Jun Huang (IEEE Trans. Knowl. Data Eng., 2021)

A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations. - Yiwen Pang; Ye Li; Sheng-Jun Huang (ACAI, 2022)

Active Learning for Open-set Annotation. - Kun-Peng Ning; Xun Zhao; Yu Li 003; Sheng-Jun Huang (CVPR, 2022)

Active Learning for Multiple Target Models. - Ying-Peng Tang; Sheng-Jun Huang (NeurIPS, 2022)

Can Adversarial Training Be Manipulated By Non-Robust Features? - Lue Tao; Lei Feng 006; Hongxin Wei; Jinfeng Yi; Sheng-Jun Huang; Songcan Chen (NeurIPS, 2022)

Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels. - Ming-Kun Xie; Jiahao Xiao; Sheng-Jun Huang (NeurIPS, 2022)

Meta Objective Guided Disambiguation for Partial Label Learning. - Bo-Shi Zou; Ming-Kun Xie; Sheng-Jun Huang (CoRR, 2022)

Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting. - Chen-Chen Zong; Zheng-Tao Cao; Hong-Tao Guo; Yun Du; Ming-Kun Xie; Shao-Yuan Li; Sheng-Jun Huang (CoRR, 2022)

Improving deep label noise learning with dual active label correction. - Shaoyuan Li; Ye Shi 004; Sheng-Jun Huang; Songcan Chen (Mach. Learn., 2022)

Partial Multi-Label Learning With Noisy Label Identification. - Ming-Kun Xie; Sheng-Jun Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks. - Ye Li; Songcan Chen; Sheng-Jun Huang (AAAI, 2023)

Multi-Label Knowledge Distillation. - Penghui Yang 001; Ming-Kun Xie; Chen-Chen Zong; Lei Feng 006; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICCV, 2023)

Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery. - Yuyan Zhou; Dong Liang 008; Songcan Chen; Sheng-Jun Huang; Shuo Yang; Chongyi Li (ICCV, 2023)

ALL-E: Aesthetics-guided Low-light Image Enhancement. - Ling Li 010; Dong Liang 008; Yuanhang Gao; Sheng-Jun Huang; Songcan Chen (IJCAI, 2023)

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. - Ming-Kun Xie; Jiahao Xiao; Hao-Zhe Liu; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (NeurIPS, 2023)

CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise. - Ming-Kun Xie; Sheng-Jun Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection. - Dong Liang 008; Jing-Wei Zhang; Ying-Peng Tang; Sheng-Jun Huang (IEEE Trans. Geosci. Remote. Sens., 2023)

Learning from crowds with sparse and imbalanced annotations. - Ye Shi 004; Shao-Yuan Li; Sheng-Jun Huang (Mach. Learn., 2023)

QBox: Partial Transfer Learning With Active Querying for Object Detection. - Ying-Peng Tang; Xiu-Shen Wei; Borui Zhao; Sheng-Jun Huang (IEEE Trans. Neural Networks Learn. Syst., 2023)

Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. - Wenhai Wan; Xinrui Wang 003; Ming-Kun Xie; Shao-Yuan Li; Sheng-Jun Huang; Songcan Chen (AAAI, 2024)

Dirichlet-Based Prediction Calibration for Learning with Noisy Labels. - Chen-Chen Zong; Ye-Wen Wang; Ming-Kun Xie; Sheng-Jun Huang (AAAI, 2024)

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. - Jiahao Xiao; Ming-Kun Xie; Heng-Bo Fan; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ECCV (52), 2024)

Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation. - Chen-Chen Zong; Ye-Wen Wang; Kun-Peng Ning; Haibo Ye; Sheng-Jun Huang (ECCV (28), 2024)

One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models. - Sheng-Jun Huang; Yi Li; Yiming Sun; Ying-Peng Tang (ICLR, 2024)

Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation. - Ye-Wen Wang; Chen-Chen Zong; Ming-Kun Xie; Sheng-Jun Huang (ICME, 2024)

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. - Ming-Kun Xie; Jiahao Xiao; Pei Peng 005; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICML, 2024)

Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations. - Ye Li; Siqi Chen; Bin Shan; Sheng-Jun Huang (IJCAI, 2024)

NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes. - Shiji Zhao; Shao-Yuan Li; Sheng-Jun Huang (IJCAI, 2024)

Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning. - Hao-Zhe Liu; Ming-Kun Xie; Chen-Chen Zong; Sheng-Jun Huang (KDD, 2024)

Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL. - Qin-Wen Luo; Ming-Kun Xie; Ye-Wen Wang; Sheng-Jun Huang (NeurIPS, 2024)

Relative difficulty distillation for semantic segmentation. - Dong Liang 008; Yue Sun; Yun Du; Songcan Chen; Sheng-Jun Huang (Sci. China Inf. Sci., 2024)

Empowering Language Models with Active Inquiry for Deeper Understanding. - Jing-Cheng Pang; Heng-Bo Fan; Pengyuan Wang; Jiahao Xiao; Nan Tang; Si-Hang Yang; Chengxing Jia; Sheng-Jun Huang; Yang Yu 001 (CoRR, 2024)

CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs. - Weijie Lv; Xuan Xia; Sheng-Jun Huang (CoRR, 2024)

Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head. - Penghui Yang 001; Chen-Chen Zong; Sheng-Jun Huang; Lei Feng 006; Bo An 001 (CoRR, 2024)

Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning. - Heng-Bo Fan; Ming-Kun Xie; Jiahao Xiao; Sheng-Jun Huang (CoRR, 2024)

A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation. - Feng Sun; Ming-Kun Xie; Sheng-Jun Huang (Mach. Intell. Res., 2024)

Sequential Cooperative Distillation for Imbalanced Multi-Task Learning. - Quan Feng; Jia-Yu Yao; Ming-Kun Xie; Sheng-Jun Huang; Songcan Chen (J. Comput. Sci. Technol., 2024)

UNM: A Universal Approach for Noisy Multi-Label Learning. - Jia-Yao Chen; Shao-Yuan Li; Sheng-Jun Huang; Songcan Chen; Lei Wang 226; Ming-Kun Xie (IEEE Trans. Knowl. Data Eng., 2024)

Denoised Graph Collaborative Filtering via Neighborhood Similarity and Dynamic Thresholding. - Haibo Ye; Lijun Zhang; Yuan Yao 001; Sheng-Jun Huang (IEEE Trans. Big Data, 2024)

StructSR: Refuse Spurious Details in Real-World Image Super-Resolution. - Yachao Li; Dong Liang 008; Tianyu Ding; Sheng-Jun Huang (AAAI, 2025)

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 (AAAI, 2025)

Improving Generalization of Deep Neural Networks by Optimum Shifting. - Yuyan Zhou; Ye Li; Lei Feng 006; Sheng-Jun Huang (AAAI, 2025)

Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning. - Yuxia Geng; Runkai Zhu; Jiaoyan Chen 001; Jintai Chen; Xiang Chen 016; Zhuo Chen 007; Shuofei Qiao; Yuxiang Wang 001; Xiaoliang Xu; Sheng-Jun Huang (ACL (Findings), 2025)

Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach. - Chen-Chen Zong; Sheng-Jun Huang (CVPR, 2025)

Learning with Partial Labels from Conflict-Free and Semi-Supervised Perspective. - Chen-Chen Zong; Sheng-Jun Huang (ICASSP, 2025)

FLAIN: Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons. - Binbin Ding; Penghui Yang 001; Sheng-Jun Huang (ICMR, 2025)

VI-PINNs: Variance-involved physics-informed neural networks for fast and accurate prediction of partial differential equations. - Bin Shan; Ye Li; Sheng-Jun Huang (Neurocomputing, 2025)

Continual learning in the presence of repetition. - Hamed Hemati; Lorenzo Pellegrini; Xiaotian Duan; Zixuan Zhao; Fangfang Xia; Marc Masana; Benedikt Tscheschner; Eduardo E. Veas; Yuxiang Zheng; Shiji Zhao; Shao-Yuan Li; Sheng-Jun Huang; et al. (Neural Networks, 2025)

Prototypes as Anchors: Tackling Unseen Noise for online continual learning. - Shaoyuan Li; Yuxiang Zheng; Sheng-Jun Huang; Songcan Chen; Kangkan Wang (Neural Networks, 2025)

Handling Noisy Annotation for Remote Sensing Semantic Segmentation via Boundary-Aware Knowledge Distillation. - Yue Sun; Dong Liang 008; Shao-Yuan Li; Songcan Chen; Sheng-Jun Huang (IEEE Trans. Geosci. Remote. Sens., 2025)

Diffusion-Noise-Based Augmentation for Long-Tailed Remote Sensing Image Classification. - Qianqian Wang; Haibo Ye; Dong Liang 008; Sheng-Jun Huang (IEEE Trans. Geosci. Remote. Sens., 2025)

Interactive Large Language Models for Reliable Answering under Incomplete Context. - Jing-Cheng Pang; Heng-Bo Fan; Pengyuan Wang; Jiahao Xiao; Nan Tang; Si-Hang Yang; Chengxing Jia; Ming-Kun Xie; Xiang Chen; Sheng-Jun Huang; Yang Yu 001 (Trans. Mach. Learn. Res., 2025)

Robust domain adaptation with noisy and shifted label distribution. - Shao-Yuan Li; Shi-Ji Zhao; Zheng-Tao Cao; Sheng-Jun Huang; Songcan Chen (Frontiers Comput. Sci., 2025)

Data-efficient LLM Fine-tuning for Code Generation. - Weijie Lv; Xuan Xia; Sheng-Jun Huang (CoRR, 2025)

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL. - Qin-Wen Luo; Ming-Kun Xie; Ye-Wen Wang; Sheng-Jun Huang (CoRR, 2025)

Efficient Code LLM Training via Distribution-Consistent and Diversity-Aware Data Selection. - Weijie Lyu; Sheng-Jun Huang; Xuan Xia (CoRR, 2025)

Aesthetics-Guided Low-Light Enhancement. - Dong Liang 008; Yuanhang Gao; Ling Li 010; Zhengyan Xu; Sheng-Jun Huang; Songcan Chen (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

A Unified Open Adapter for Open-World Noisy Label Learning: Data-Centric and Learning-Based Insights. - Chen-Chen Zong; Penghui Yang 001; Ming-Kun Xie; Sheng-Jun Huang (IEEE Trans. Circuits Syst. Video Technol., 2025)

Image Lens Flare Removal Using Adversarial Curve Learning. - Yuyan Zhou; Dong Liang 008; Songcan Chen; Sheng-Jun Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2025)