Semi-Supervised Regression with Co-Training. - Zhi-Hua Zhou; Ming Li 005 (IJCAI, 2005)

SETRED: Self-training with Editing. - Ming Li 005; Zhi-Hua Zhou (PAKDD, 2005)

Multi-Instance Learning Based Web Mining. - Zhi-Hua Zhou; Kai Jiang; Ming Li 005 (Appl. Intell., 2005)

Tri-Training: Exploiting Unlabeled Data Using Three Classifiers. - Zhi-Hua Zhou; Ming Li 005 (IEEE Trans. Knowl. Data Eng., 2005)

Mining Frequent Patterns based on Compressed FP-tree without Conditional FP-tree Generation. - Fei Chen; Lin Shang 001; Ming Li 005; Zhaoqian Chen; Shifu Chen (GrC, 2006)

Generation of Comprehensible Hypotheses from Gene Expression Data. - Yuan Jiang 001; Ming Li 005; Zhi-Hua Zhou (BioDM, 2006)

Predicting Future Customers via Ensembling Gradually Expanded Trees. - Yang Yu 001; De-Chuan Zhan; Xu-Ying Liu; Ming Li 005; Zhi-Hua Zhou (Int. J. Data Warehous. Min., 2007)

Semisupervised Regression with Cotraining-Style Algorithms. - Zhi-Hua Zhou; Ming Li 005 (IEEE Trans. Knowl. Data Eng., 2007)

Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples. - Ming Li 005; Zhi-Hua Zhou (IEEE Trans. Syst. Man Cybern. Part A, 2007)

Mining Bulletin Board Systems Using Community Generation. - Ming Li 005; Zhongfei (Mark) Zhang; Zhi-Hua Zhou (PAKDD, 2008)

Online Manifold Regularization: A New Learning Setting and Empirical Study. - Andrew B. Goldberg; Ming Li 005; Xiaojin Zhu 001 (ECML/PKDD (1), 2008)

Learning instance specific distances using metric propagation. - De-Chuan Zhan; Ming Li 005; Yufeng Li 008; Zhi-Hua Zhou (ICML, 2009)

Exploiting Multi-Modal Interactions: A Unified Framework. - Ming Li 005; Xiao-Bing Xue; Zhi-Hua Zhou (IJCAI, 2009)

Semi-supervised document retrieval. - Ming Li 005; Hang Li 001; Zhi-Hua Zhou (Inf. Process. Manag., 2009)

Mining extremely small data sets with application to software reuse. - Yuan Jiang 001; Ming Li 005; Zhi-Hua Zhou (Softw. Pract. Exp., 2009)

Exploiting remote learners in Internet environment with agents. - Ming Li 005; Wei Wang 028; Zhi-Hua Zhou (Sci. China Inf. Sci., 2010)

Semi-supervised learning by disagreement. - Zhi-Hua Zhou; Ming Li 005 (Knowl. Inf. Syst., 2010)

Software Defect Detection with Rocus. - Yuan Jiang 001; Ming Li 005; Zhi-Hua Zhou (J. Comput. Sci. Technol., 2011)

Sample-based software defect prediction with active and semi-supervised learning. - Ming Li 005; Hongyu Zhang 002; Rongxin Wu; Zhi-Hua Zhou (Autom. Softw. Eng., 2012)

PerGrab: Adapting Grabbing Gesture Recognition for Personalized Non-contact HCI. - Tao Li; Ming Li 005 (IScIDE, 2013)

Constrained feature selection for localizing faults. - Tien-Duy B. Le; David Lo 001; Ming Li 005 (ICSME, 2015)

Improving Software Quality and Productivity Leveraging Mining Techniques: [Summary of the Second Workshop on Software Mining, at ASE 2013]. - Ming Li 005; Hongyu Zhang 002; David Lo 001; Lucia (ACM SIGSOFT Softw. Eng. Notes, 2015)

Learning Unified Features from Natural and Programming Languages for Locating Buggy Source Code. - Xuan Huo; Ming Li 005; Zhi-Hua Zhou (IJCAI, 2016)

Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code. - Xuan Huo; Ming Li 005 (IJCAI, 2017)

Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code. - Huihui Wei; Ming Li 005 (IJCAI, 2017)

Cost-effective build outcome prediction using cascaded classifiers. - Ansong Ni; Ming Li 005 (MSR, 2017)

Code Attention: Translating Code to Comments by Exploiting Domain Features. - Wenhao Zheng 001; Hong-Yu Zhou; Ming Li 005; Jianxin Wu 001 (CoRR, 2017)

The best answer prediction by exploiting heterogeneous data on software development Q&A forum. - Wenhao Zheng 001; Ming Li 005 (Neurocomputing, 2017)

Semi-Supervised AUC Optimization Without Guessing Labels of Unlabeled Data. - Zheng Xie 001; Ming Li 005 (AAAI, 2018)

Holistic and Deep Feature Pyramids for Saliency Detection. - Shizhong Dong; Zhifan Gao; Shanhui Sun; Xin Wang 045; Ming Li 005; Heye Zhang; Guang Yang 006; Huafeng Liu 003; Shuo Li 001 (BMVC, 2018)

Learning Semantic Features for Software Defect Prediction by Code Comments Embedding. - Xuan Huo; Yang Yang 074; Ming Li 005; De-Chuan Zhan (ICDM, 2018)

T2S: Domain Adaptation Via Model-Independent Inverse Mapping and Model Reuse. - Zhi-Yu Shen; Ming Li 005 (ICDM, 2018)

ACONA: active online model adaptation for predicting continuous integration build failures. - Ansong Ni; Ming Li 005 (ICSE (Companion Volume), 2018)

Positive and Unlabeled Learning for Detecting Software Functional Clones with Adversarial Training. - Huihui Wei; Ming Li 005 (IJCAI, 2018)

Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization. - Zheng Xie 001; Ming Li 005 (IJCAI, 2018)

Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud. - Ya-Lin Zhang 001; Jun Zhou 011; Wenhao Zheng 001; Ji Feng; Longfei Li; Ziqi Liu; Ming Li 005; Zhiqiang Zhang 012; Chaochao Chen 001; Xiaolong Li 005; Zhi-Hua Zhou (CoRR, 2018)

Detectability and reproducibility of the olfactory fMRI signal under the influence of magnetic susceptibility artifacts in the primary olfactory cortex. - Jiaming Lu; Xin Wang 045; Zhao Qing; Zhu Li; Wen Zhang; Ying Liu; Lihua Yuan; Le Cheng; Ming Li 005; Bin Zhu 008; Xin Zhang 013; Qing X. Yang; et al. (NeuroImage, 2018)

Automatic Code Review by Learning the Revision of Source Code. - Shu-Ting Shi; Ming Li 005; David Lo 001; Ferdian Thung; Xuan Huo (AAAI, 2019)

Find Me if You Can: Deep Software Clone Detection by Exploiting the Contest between the Plagiarist and the Detector. - Yan-Ya Zhang; Ming Li 005 (AAAI, 2019)

Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially. - Yudong Zhang; Wenhao Zheng 001; Ming Li 005 (AAAI, 2019)

On the Robust Splitting Criterion of Random Forest. - Bin-Bin Yang; Wei Gao 008; Ming Li 005 (ICDM, 2019)

Recurrent Aggregation Learning for Multi-view Echocardiographic Sequences Segmentation. - Ming Li 005; Weiwei Zhang 006; Guang Yang 006; Chengjia Wang; Heye Zhang; Huafeng Liu 003; Wei Zheng; Shuo Li 001 (MICCAI (2), 2019)

Towards One Reusable Model for Various Software Defect Mining Tasks. - Heng-Yi Li; Ming Li 005; Zhi-Hua Zhou (PAKDD (3), 2019)

DeepReview: Automatic Code Review Using Deep Multi-instance Learning. - Heng-Yi Li; Shu-Ting Shi; Ferdian Thung; Xuan Huo; Bowen Xu; Ming Li 005; David Lo 001 (PAKDD (2), 2019)

Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness. - Lan-Zhe Guo; Yufeng Li 008; Ming Li 005; Jinfeng Yi; Bowen Zhou 001; Zhi-Hua Zhou (CoRR, 2019)

CodeAttention: translating source code to comments by exploiting the code constructs. - Wenhao Zheng 001; Hong-Yu Zhou; Ming Li 005; Jianxin Wu 001 (Frontiers Comput. Sci., 2019)

On cost-effective software defect prediction: Classification or ranking? - Xuan Huo; Ming Li 005 (Neurocomputing, 2019)

Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud. - Ya-Lin Zhang 001; Jun Zhou 011; Wenhao Zheng 001; Ji Feng; Longfei Li; Ziqi Liu; Ming Li 005; Zhiqiang Zhang 012; Chaochao Chen 001; Xiaolong Li 005; Yuan (Alan) Qi; Zhi-Hua Zhou (ACM Trans. Intell. Syst. Technol., 2019)

Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization. - Xuan Huo; Ming Li 005; Zhi-Hua Zhou (AAAI, 2020)

Deep Time-Stream Framework for Click-through Rate Prediction by Tracking Interest Evolution. - Shu-Ting Shi; Wenhao Zheng 001; Jun Tang; Qing-Guo Chen; Yao Hu; Jianke Zhu; Ming Li 005 (AAAI, 2020)

Learning Code Changes by Exploiting Bidirectional Converting Deviation. - Jia-Wei Mi; Shu-Ting Shi; Ming Li 005 (ACML, 2020)

Unified model for interpreting multi-view echocardiographic sequences without temporal information. - Ming Li 005; Shizhou Dong; Zhifan Gao; Cheng Feng; Huahua Xiong; Wei Zheng; Dhanjoo N. Ghista; Heye Zhang; Victor Hugo C. de Albuquerque (Appl. Soft Comput., 2020)

MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis. - Ming Li 005; Chengjia Wang; Heye Zhang; Guang Yang 006 (Comput. Biol. Medicine, 2020)

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We? - Simin Wang; Liguo Huang; Jidong Ge; Tengfei Zhang; Haitao Feng; Ming Li 005; He Zhang 001; Vincent Ng 001 (CoRR, 2020)

Automatic fetal brain extraction from 2D in utero fetal MRI slices using deep neural network. - Jinpeng Li; Yishan Luo; Lin Shi 001; Xin Zhang 013; Ming Li 005; Bing Zhang 012; Defeng Wang (Neurocomputing, 2020)

Enhancing supervised bug localization with metadata and stack-trace. - Yaojing Wang; Yuan Yao 001; Hanghang Tong; Xuan Huo; Ming Li 005; Feng Xu 007; Jian Lu 001 (Knowl. Inf. Syst., 2020)

Towards a new generation of artificial intelligence in China. - Fei Wu 001; Cewu Lu; Mingjie Zhu; Hao Chen 011; Jun Zhu 001; Kai Yu 004; Lei Li 005; Ming Li 005; Qianfeng Chen; Xi Li 001; Xudong Cao; Zhongyuan Wang 006; et al. (Nat. Mach. Intell., 2020)

Enhancing Context-Based Meta-Reinforcement Learning Algorithms via An Efficient Task Encoder (Student Abstract). - Feng Xu 007; Shengyi Jiang; Hao Yin; Zongzhang Zhang; Yang Yu 001; Ming Li 005; Dong Li 016; Wulong Liu (AAAI, 2021)

Towards Generating Summaries for Lexically Confusing Code through Code Erosion. - Fan Yan; Ming Li 005 (IJCAI, 2021)

Deep Transfer Bug Localization. - Xuan Huo; Ferdian Thung; Ming Li 005; David Lo 001; Shu-Ting Shi (IEEE Trans. Software Eng., 2021)

Learning from the Multi-Level Abstraction of the Control Flow Graph via Alternating Propagation for Bug Localization. - Yi-Fan Ma; Ming Li 005 (ICDM, 2022)

Reduced Implication-bias Logic Loss for Neuro-Symbolic Learning. - Haoyuan He 001; Wang-Zhou Dai; Ming Li 005; Yu Liu 083; Yongchang Ma (CoRR, 2022)

RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction. - Liang Jin; Shixuan Gu; Donglai Wei 001; Kaiming Kuang; Hanspeter Pfister; Bingbing Ni; Jiancheng Yang; Ming Li 005 (CoRR, 2022)

The flowing nature matters: feature learning from the control flow graph of source code for bug localization. - Yi-Fan Ma; Ming Li 005 (Mach. Learn., 2022)

Cooperative and Adversarial Learning: Co-enhancing Discriminability and Transferability in Domain Adaptation. - Hui Sun 003; Zheng Xie 001; Xin-Ye Li; Ming Li 005 (AAAI, 2023)

Semi-supervised Learning with Support Isolation by Small-Paced Self-Training. - Zheng Xie 001; Hui Sun 003; Ming Li 005 (AAAI, 2023)

Beyond Lexical Consistency: Preserving Semantic Consistency for Program Translation. - Yali Du 002; Yi-Fan Ma; Zheng Xie 001; Ming Li 005 (ICDM, 2023)

CHRONOS: Time-Aware Zero-Shot Identification of Libraries from Vulnerability Reports. - Yunbo Lyu; Thanh Le-Cong; Hong Jin Kang; Ratnadira Widyasari; Zhipeng Zhao; Xuan-Bach Dinh Le 001; Ming Li 005; David Lo 001 (ICSE, 2023)

Capturing the Long-Distance Dependency in the Control Flow Graph via Structural-Guided Attention for Bug Localization. - Yi-Fan Ma; Yali Du 002; Ming Li 005 (IJCAI, 2023)

Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation. - Xin-Ye Li; Jiang-Tian Xue; Zheng Xie 001; Ming Li 005 (CoRR, 2023)

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data. - Tianyang Zhong; Wei Zhao 040; Yutong Zhang; Yi Pan 001; Peixin Dong; Zuowei Jiang; Xiaoyan Kui; Youlan Shang; Li Yang; Yaonai Wei; Longtao Yang; Hao Chen 011; et al. (CoRR, 2023)

APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation. - Chengran Yang; Jiakun Liu; Bowen Xu; Christoph Treude; Yunbo Lyu; Junda He; Ming Li 005; David Lo 001 (CoRR, 2023)

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels. - Ming Li 005; Yingying Fang; Zeyu Tang 001; Chibudom Onuorah; Jun Xia 002; Javier Del Ser; Simon Walsh; Guang Yang 006 (IEEE Trans. Emerg. Top. Comput. Intell., 2023)

Machine/Deep Learning for Software Engineering: A Systematic Literature Review. - Simin Wang; Liguo Huang; Amiao Gao; Jidong Ge; Tengfei Zhang; Haitao Feng; Ishna Satyarth; Ming Li 005; He Zhang 001; Vincent Ng 001 (IEEE Trans. Software Eng., 2023)

Enhancing unsupervised domain adaptation by exploiting the conceptual consistency of multiple self-supervised tasks. - Hui Sun 003; Ming Li 005 (Sci. China Inf. Sci., 2023)

AUC Optimization from Multiple Unlabeled Datasets. - Zheng Xie 001; Yu Liu 083; Ming Li 005 (AAAI, 2024)

Ambiguity-Aware Abductive Learning. - Hao-Yuan He 001; Hui Sun 003; Zheng Xie 001; Ming Li 005 (ICML, 2024)

Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization. - Rui Kong; Chenyang Wu 001; Chen-Xiao Gao; Zongzhang Zhang; Ming Li 005 (IJCAI, 2024)

Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation. - Ming Li 005; Guang Yang 006 (ISBI, 2024)

A Joint Learning Model with Variational Interaction for Multilingual Program Translation. - Yali Du 002; Hui Sun 003; Ming Li 005 (ASE, 2024)

Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space. - Yu Liu 083; Qinglin Jia; Shuting Shi; Chuhan Wu; Zhaocheng Du; Zheng Xie 001; Ruiming Tang; Muyu Zhang; Ming Li 005 (RecSys, 2024)

Weakly Supervised AUC Optimization: A Unified Partial AUC Approach. - Zheng Xie 001; Yu Liu 083; Hao-Yuan He 001; Ming Li 005; Zhi-Hua Zhou (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction. - Liang Jin; Shixuan Gu; Donglai Wei 001; Jason Ken Adhinarta; Kaiming Kuang; Yongjie Jessica Zhang; Hanspeter Pfister; Bingbing Ni; Jiancheng Yang; Ming Li 005 (IEEE Trans. Medical Imaging, 2024)

Reduced implication-bias logic loss for neuro-symbolic learning. - Hao-Yuan He 001; Wang-Zhou Dai; Ming Li 005 (Mach. Learn., 2024)

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status. - Wei Zhao 040; Weidao Chen; Ge Li; Du Lei; Jiancheng Yang; Yanjing Chen; Yingjia Jiang; Jiangfen Wu; Bingbing Ni; Yeqi Sun; Shaokang Wang; Yingli Sun; et al. (IEEE Trans. Neural Networks Learn. Syst., 2024)

From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare. - Ming Li 005; Pengcheng Xu; Junjie Hu; Zeyu Tang 001; Guang Yang 006 (Medical Image Anal., 2025)

Probabilistic instance dependent label refinement for noisy label learning. - Hao-Yuan He 001; Yu Liu 083; Renbiao Liu; Zheng Xie 001; Ming Li 005 (Mach. Learn., 2025)

MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs. - Hui Sun 003; Shiyin Lu; Huanyu Wang; Qing-Guo Chen; Zhao Xu; Weihua Luo; Kaifu Zhang; Ming Li 005 (CoRR, 2025)

A Learnability Analysis on Neuro-Symbolic Learning. - Hao-Yuan He 001; Ming Li 005 (CoRR, 2025)

Post-Incorporating Code Structural Knowledge into LLMs via In-Context Learning for Code Translation. - Yali Du 002; Hui Sun 003; Ming Li 005 (CoRR, 2025)

Enhancing LLMs in Long Code Translation through Instrumentation and Program State Alignment. - Xin-Ye Li; Ya-Li Du 002; Ming Li 005 (CoRR, 2025)

Capturing the context-aware code change via dynamic control flow graph for commit message generation. - Yali Du 002; Ying Li; Yi-Fan Ma; Ming Li 005 (Mach. Learn., 2025)

Deep Rib Fracture Instance Segmentation and Classification From CT on the RibFrac Challenge. - Jiancheng Yang; Rui Shi; Liang Jin; Xiaoyang Huang; Kaiming Kuang; Donglai Wei 001; Shixuan Gu; Jianying Liu; Pengfei Liu; Zhizhong Chai; Yongjie Xiao; Hao Chen 011; et al. (IEEE Trans. Medical Imaging, 2025)