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Publications (2010-2026)

69 Software Engineering
22 Distributed AI Systems
27 Agentic AI & LLMs
2026
  • [1] Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation
    Tanghaoran Zhang, Xinjun Mao, Shangwen Wang, Yuxin Zhao, Yao Lu, Zezhou Tang, Wenyu Xu, Longfei Sun, Changrong Xie, Kang Yang, Yue Yu
    FSE 2026, Montreal, Canada CCF-A [PDF]
2025
  • [2] Are External Contributions Important to Project Productivity in Open Source Software? A Deep Insight based on Issue Entropy
    Yang Shen, Tao Wang, Xunhui Zhang, Yang Zhang, Cheng Yang, Yue Yu, Huaimin Wang
    CSCW 2025, Bergen, Norway CCF-A
  • [3] Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering
    Tanghaoran Zhang, Yue Yu, Xinjun Mao, Shangwen Wang, Kang Yang, Yao Lu, Zhang Zhang, Yuxin Zhao
    ICSE 2025, Ottawa, Canada CCF-A [PDF]
  • [4] AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation
    Tanghaoran Zhang, Xinjun Mao, Shangwen Wang, Yuxin Zhao, Yao Lu, Jin Zhang, Zhang Zhang, Kang Yang, Yue Yu
    ASE 2025, Seoul, Korea CCF-A [PDF]
2024
  • [5] How do Developers Adapt Code Snippets to Their Contexts? An Empirical Study of Context-Based Code Snippet Adaptations
    Tanghaoran Zhang, Yao Lu, Yue Yu, Xinjun Mao, Yang Zhang, Yuxin Zhao
    IEEE TSE, 2024 CCF-A
  • [6] Towards More Precise Coincidental Correctness Detection with Deep Semantic Learning
    Huan Xie, Yan Lei, Meng Yan, Shanshan Li, Xiaoguang Mao, Yue Yu, David Lo
    IEEE TSE, 2024 CCF-A
  • [7] Cut to the Chase: An Error-Oriented Approach to Detect Error-Handling Bugs
    Haoran Liu, Zhouyang Jia, Shanshan Li, Yan Lei, Yue Yu, Yu Jiang, Xiaoguang Mao, Liao Xiangke
    FSE 2024, Porto de Galinhas, Brazil CCF-A [PDF]
2023
  • [8] PLUMBER: Boosting the Propagation of Vulnerability Fixes in the npm Ecosystem
    Ying Wang, Peng Sun, Lin Pei, Yue Yu, Chang Xu, Shing-Chi Cheung, Hai Yu, Zhiliang Zhu
    IEEE TSE, 2023 CCF-A [PDF]
  • [9] Two Sides of the Same Coin: Exploiting the Impact of Identifiers in Neural Code Comprehension
    Shuzheng Gao, Cuiyun Gao, Chaozheng Wang, Jun Sun, David Lo, Yue Yu
    ICSE 2023, Melbourne, Australia CCF-A [PDF]
  • [10] Understanding and Detecting On-the-Fly Configuration Bugs
    Teng Wang, Zhouyang Jia, Shanshan Li, Si Zheng, Yue Yu, Erci Xu, Shaoliang Peng, Xiangke Liao
    ICSE 2023, Melbourne, Australia CCF-A [PDF]
  • [11] MulCS: Towards a Unified Deep Representation for Multilingual Code Search
    Yingwei Ma, Yue Yu, Shanshan Li, Zhouyang Jia, Jun Ma, Rulin Xu, Wei Dong, Xiangke Liao
    SANER 2023, Macao, China CCF-B [PDF]
  • [12] Can Machine Learning Pipelines Be Better Configured?
    Yibo Wang, Ying Wang, Tingwei Zhang, Yue Yu, Shing-Chi Cheung, Hai Yu, Zhiliang Zhu
    ESEC/FSE 2023, San Francisco, USA CCF-A [PDF]
  • [13] When Database Meets New Storage Devices: Understanding and Exposing Performance Mismatches via Configurations
    Haochen He, Erci Xu, Shanshan Li, Zhouyang Jia, Si Zheng, Yue Yu, Jun Ma, Xiangke Liao
    VLDB 2023, Vancouver, Canada CCF-A [PDF]
  • [14] A Two-Stage Framework for Ambiguous Classification in Software Engineering
    Jiaying Li, Yan Lei, Shanshan Li, Haifang Zhou, Yue Yu, Zhouyang Jia, Yingwei Ma, Teng Wang
    ISSRE 2023, Florence, Italy CCF-B
2022
  • [15] To Follow or Not to Follow: Understanding Issue/Pull-Request Templates on GitHub
    Zhixing Li, Yue Yu, Tao Wang, Lei Yan, Ying Wang, Huaimin Wang
    IEEE TSE, 2022 CCF-A [PDF]
  • [16] Pull Request Decisions Explained: An Empirical Overview
    Xunhui Zhang, Yue Yu, Georgios Gousios, Ayushi Rastogi
    IEEE TSE, 2022 CCF-A [PDF]
  • [17] Opportunities and Challenges in Repeated Revisions to Pull-Requests: An Empirical Study
    Zhixing Li, Yue Yu, Tao Wang, Shanshan Li, Huaimin Wang
    CSCW 2022, Taipei, China CCF-A [PDF]
  • [18] Who, What, Why and How? Towards the Monetary Incentive in Crowd Collaboration: A Case Study of Github's Sponsor Mechanism
    Xunhui Zhang, Tao Wang, Yue Yu, Qiubing Zeng, Zhixing Li, Huaimin Wang
    CHI 2022, New Orleans, USA CCF-A [PDF]
  • [19] Multi-Intention-Aware Configuration Selection for Performance Tuning
    Haochen He, Zhouyang Jia, Shanshan Li, Yue Yu, Chenglong Zhou, Qing Liao, Ji Wang, Xiangke Liao
    ICSE 2022, Pittsburgh, USA CCF-A [PDF]
  • [20] Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
    Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, Xiangke Liao
    ICSE 2022, Pittsburgh, USA CCF-A [PDF]
  • [21] A Universal Data Augmentation Approach for Fault Localization
    Huan Xie, Yan Lei, Meng Yan, Yue Yu, Xin Xia, Xiaoguang Mao
    ICSE 2022, Pittsburgh, USA CCF-A [PDF]
  • [22] Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach
    Zhuo Zhang, Yan Lei, Meng Yan, Yue Yu, Jiachi Chen, Shangwen Wang, Xiaoguang Mao
    ASE 2022, Michigan, USA CCF-A
  • [23] Towards Usable Neural Comment Generation via Code-comment Linkage Interpretation: Method and Empirical Study
    Shuyao Jiang, Jiacheng Shen, Shengnan Wu, Yu Cai, Yue Yu, Yangfan Zhou
    IEEE TSE, 2022 CCF-A [PDF]
  • [24] Influential Global and Local Contexts Guided Trace Representation for Fault Localization
    Zhuo Zhang, Yan Lei, Ting Su, Meng Yan, Xiaoguang Mao, Yue Yu
    ACM TOSEM, 2022 CCF-A [PDF]
  • [25] FENSE: A Feature-Based Ensemble Modeling Approach to Cross-Project Just-in-Time Defect Prediction
    Tanghaoran Zhang, Yue Yu, Xinjun Mao, Yao Lu, Zhixing Li, Huaimin Wang
    EMSE, 2022 CCF-B [PDF]
  • [26] deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search
    Chen Zeng, Yue Yu, Shanshan Li, Xin Xia, Zhiming Wang, Mingyang Geng, Linxiao Bai, Wei Dong, Xiangke Liao
    ACM TOSEM, 2022 CCF-A [PDF]
2021
  • [27] Are You Still Working on This? An Empirical Study on Pull Request Abandonment
    Zhixing Li, Yue Yu, Tao Wang, Gang Yin, Shanshan Li, Huaimin Wang
    IEEE TSE, 2021 CCF-A
  • [28] MulCode: A Multi-task Learning Approach for Source Code Understanding
    Deze Wang, Yue Yu, Shanshan Li, Wei Dong, Ji Wang, Qing liao
    SANER 2021 CCF-B
  • [29] How to Cherry Pick the Bug Report for Better Summarization?
    Haoran Liu, Yue Yu, Shanshan Li, Mingyang Gen, Xiaoguang Mao, Xiangke Liao
    EMSE, 2021 CCF-B
  • [30] Dual Channel Among Task and Contribution on OSS Communities: An Empirical Study
    Yu Zhang, Yue Yu, Tao Wang, Zhixing Li, Xiaochuan Wang
    IJSEKE, 2021 CCF-C
2020
  • [31] Redundancy, Context, and Preference: An Empirical Study of Duplicate Pull Requests in OSS Projects
    Zhixing Li, Yue Yu, Minghui Zhou, Tao Wang, Gang Yin, Long Lan, Huaimin Wang
    IEEE TSE, 2020 CCF-A
  • [32] BugSum: Deep Context Understanding for Bug Report Summarization
    Haoran Liu, Yue Yu, Shanshan Li, Yong Guo, Deze Wang, Xiaoguang Mao
    ICPC 2020 CCF-B
  • [33] On the Shoulders of Giants: A New Dataset for Pull-based Development Research
    Xunhui Zhang, Ayushi Rastogi, Yue Yu
    MSR 2020 CCF-C
  • [34] Software Visualization and Deep Transfer Learning for Effective Software Defect Prediction
    Jinyin Chen, Keke Hu, Yue Yu, Zhuangzhi Chen, Qi Xuan, Yi Liu, Vladimir Filkov
    ICSE 2020 CCF-A
  • [35] CP-Detector: Using Configuration-related Performance Properties to Expose Performance Bugs
    Haochen He, Zhouyang Jia, Shanshan Li, Erci Xu, Tingting Yu, Yue Yu, Ji Wang, Xiangke Liao
    ASE 2020 CCF-A
  • [36] Crowd-intelligence-based software development method and practices
    Tao Wang, Gang Yin, Yue Yu, Yang Zhang, Huaimin Wang
    Science China Information Sciences, 2020 CCF-B
2019
  • [37] Detecting Duplicate Contributions in Pull-based Model Combining Textual and Change Similarities
    Zhixing Li, Yue Yu, Tao Wang, Gang Yin, Xinjun Mao, Huaimin Wang
    JCST, 2019 CCF-B
  • [38] Why API Documentation is Insufficient for Developers: an Empirical Study
    Fan Qiang, Yue Yu, Tao Wang, Gang Yin, Huaimin Wang
    Science China Information Sciences, 2019 CCF-B
  • [39] HAF: A Hybrid Annotation Framework Based on Expert Knowledge and Learning Technique
    Zhixing Li, Yue Yu, Tao Wang, Gang Yin, Xinjun Mao, Huaimin Wang
    Science China Information Sciences, 2019 CCF-B
  • [40] Multi-reviewing pull-requests: An exploratory study on GitHub OSS projects
    Dongyang Hu, Yang Zhang, Junsheng Chang, Gang Yin, Yue Yu, Tao Wang
    IST, 2019 CCF-B
2018
  • [41] A Dataset of Duplicate Pull-requests in GitHub
    Yue Yu, Zhixing Li, Gang Yin, Tao Wang, Huaimin Wang
    MSR 2018, Gothenburg, Sweden CCF-C
  • [42] Within-Ecosystem Issue Linking: A Large-scale Study of Rails
    Yang Zhang, Yue Yu, Huaimin Wang, Bogdan Vasilescu, Vladimir Filkov
    SoftwareMining@ASE 2018, France
  • [43] A Hybrid Approach for Tag Hierarchy Construction
    Shangwen Wang, Tao Wang, Xiaoguang Mao, Gang Yin, Yue Yu
    ICSR 2018, Madrid, Spain
  • [44] Transferring Well-Trained Models for Cross-Project Issue Classification: A Large-Scale Empirical Study
    Yue Yu, Yarong Zeng, Qiang Fan, Huaimin Wang
    Internetware 2018, Beijing, China
2017
  • [45] Where is the Road for Issue Reports Classification Based on Text Mining
    Qiang Fan, Yue Yu, Gang Yin, Tao Wang, Huaimin Wang
    ESEM 2017, Toronto, Canada CCF-B
  • [46] Automatic Classification of Review Comments in Pull-based Development Model
    Zhixing Li, Yue Yu, Gang Yin, Tao Wang, Qiang Fan, Huaimin Wang
    SEKE 2017, Pittsburgh, USA CCF-C
  • [47] What are they talking about? Analyzing Code Reviews in Pull-based Development Model
    Zhixing Li, Yue Yu, Gang Yin, Tao Wang, Huaimin Wang
    JCST, 2017 CCF-B
  • [48] DevRec: A Developer Recommendation System for Open Source Repositories
    Xunhui Zhang, Tao Wang, Gang Yin, Cheng Yang, Yue Yu, Huaimin Wang
    ICSR 2017 CCF-C
  • [49] Detecting duplicate pull-requests in GitHub
    Zhixing Li, Gang Yin, Yue Yu, Tao Wang, Huaimin Wang
    Internetware 2017
2016
  • [50] Reviewer Recommendation for Pull-Requests in GitHub: What Can We Learn from Code Review and Bug Assignment?
    Yue Yu, Huaimin Wang, Gang Yin, Tao Wang
    IST, 2016 CCF-B
  • [51] Determinants of pull-based development in the context of continuous integration
    Yue Yu, Gang Yin, Tao Wang, Cheng Yang, Huaimin Wang
    Science China Information Sciences, 2016 CCF-B
  • [52] Social media in GitHub: the role of @-mention in assisting software development
    Yang Zhang, Huaimin Wang, Gang Yin, Tao Wang, Yue Yu
    Science China Information Sciences, 2016 CCF-B
  • [53] Correlation-based software search by leveraging software term database
    Zhixing Li, Gang Yin, Yang Zhang, Yue Yu, Huaimin Wang
    FCS, 2016 CCF-C
2015
  • [54] Quality and Productivity Outcomes Relating to Continuous Integration in GitHub
    Bogdan Vasilescu, Yue Yu, Huaimin Wang, Prem Devanbu, Vladimir Filkov
    ESEC/FSE 2015, Bergamo, Italy CCF-A
  • [55] Wait For It: Determinants of Pull Request Evaluation Latency on GitHub
    Yue Yu, Huaimin Wang, Vladimir Filkov, Prem Devanbu, Bogdan Vasilescu
    MSR 2015, Florence, Italy CCF-C
  • [56] Exploring the Use of @-mention to Assist Software Development in GitHub
    Yang Zhang, Huaimin Wang, Gang Yin, Tao Wang, Yue Yu
    Internetware 2015, Wuhan, China
  • [57] Evaluating Bug Severity Using Crowd-based Knowledge: An Exploratory Study
    Yang Zhang, Huaimin Wang, Gang Yin, Tao Wang, Yue Yu
    Internetware 2015, Wuhan, China
2014
  • [58] Reviewer Recommender of Pull-Requests in GitHub
    Yue Yu, Huaimin Wang, Gang Yin, Charles X. Ling
    ICSME 2014, Victoria, Canada CCF-B
  • [59] Who Should Review This Pull-Request: Reviewer Recommendation to Expedite Crowd Collaboration
    Yue Yu, Huaimin Wang, Gang Yin, Charles X. Ling
    APSEC 2014, JEJU, KOREA CCF-C 🏆 Best Paper
  • [60] Exploring the Patterns of Social Behavior in GitHub
    Yue Yu, Huaimin Wang, Gang Yin, Tao Wang
    CrowdSoft 2014, Hong Kong
  • [61] Investigating social media in GitHub's pull-requests: a case study on Ruby on Rails
    Yang Zhang, Gang Yin, Yue Yu, Huaimin Wang
    CrowdSoft 2014, Hong Kong
  • [62] An Exploratory Study of @-mention in GitHub's Pull-requests
    Yang Zhang, Gang Yin, Yue Yu, Huaimin Wang
    APSEC 2014, JEJU, KOREA CCF-C
2013
  • [63] HESA: The Construction and Evaluation of Hierarchical Software Feature Repository
    Yue Yu, Huaimin Wang, Gang Yin, Xiang Li
    SEKE 2013, Boston, USA CCF-C
  • [64] Mining and Recommending Software Features across Multiple Web Repositories
    Yue Yu, Huaimin Wang, Gang Yin, Bo Liu
    Internetware 2013, Changsha, China
  • [65] A Trusted Remote Attestation Model based on Trusted Computing
    Yue Yu, Huaimin Wang, Bo Liu, Gang Yin
    TrustCom 2013, Melbourne, Australia CCF-C
2012
  • [66] Inducing Taxonomy from Tags: An Agglomerative Hierarchical Clustering Framework
    Xiang Li, Huaimin Wang, Gang Yin, Tao Wang, Cheng Yang, Yue Yu, Dengqing Tang
    ADMA 2012
2011
  • [67] Trusted Computing Dynamic Attestation by Using Static Analysis Based Behavior Model
    Fajiang Yu, Xianglei Tang, Yue Yu
    ISPDPAW 2011, Bussan, Korea
  • [68] Optimization of Program Behavior Model for Trusted Computing Dynamic Attestation
    Fajiang Yu, Yuewei Xu, Yue Yu
    Journal of Computational Information Systems, 2011
2010
  • [69] Static Analysis-based Behavior Model Building for Trusted Computing Dynamic Verification
    Fajiang Yu, Yue Yu
    Wuhan University Journal of Natural Sciences, 2010
2026
  • [70] ParaSync: Exploiting Fine-Grained Parallelism for Efficient File Synchronization
    Zhihao Zhang, Lu Tang, Huiba Li, Yue Yu, Jiwu Shu, Yiming Zhang
    FAST 2026, Santa Clara, USA CCF-A
  • [71] Fate: Fast Edge Inference of Mixture-of-Experts Models via Cross-Layer Gate
    Zhiyuan Fang, Xingfan Yu, Yuegui Huang, Zicong Hong, Yufeng Lyu, Wuhui Chen, Yue Yu, Fan Yu
    WWW 2026, Dubai, UAE CCF-A
2025
  • [72] Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
    Zhiyuan Fang, Yuegui Huang, Zicong Hong, Yufeng Lyu, Wuhui Chen, Yue Yu, Fan Yu, Zibin Zheng
    ASPLOS 2025, Rotterdam, Netherlands CCF-A [PDF]
  • [73] Obscura: Concealing Recomputation Overhead in Training of Large Language Models with Bubble-filling Pipeline Transformation
    Y. Huang, Y. Jiang, Z. Hong, W. Chen, B. Wang, W. Zhu, Yue Yu, Zibin Zheng
    USENIX ATC 2025, Boston, USA CCF-A [PDF]
  • [74] CrossFS: Improving Cross-Domain File System Performance with CRDT-Based Metadata Synchronization
    Qiwen Ke, Yina Lv, Zhihao Zhang, Zhirong Shen, Yue Yu, Hailiang Chen, Zhenglong Song, Xinbiao Gan, Jiaxin Li, Dongsheng Li, Xin Yao, Meiling Wang, Yiming Zhang
    ACM TOS, 2025 CCF-A
  • [75] Looking Back to Move Forward: Unveiling the Mysteries of HBM Errors to Predict Future Failures
    Shuyue Zhou, Xinbing Hu, Ronglong Wu, Jiahua Lu, Zhirong Shen, Zikang Xu, Yue Yu, Yuze Jiang, Jiwu Shu, Kunling Yang, Feilong Lin, Yiming Zhang
    ACM TOS, 2025 CCF-A
  • [76] NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
    Xiaohan Bi, Binhang Qi, Hailong Sun, Xiang Gao, Yue Yu, Xiaojun Liang
    ACM TOSEM, 2025 CCF-A [PDF]
  • [77] RingMoE: Mixture-of-modality-experts multi-modal foundation models for universal remote sensing image interpretation
    Hanbo Bi, Yingchao Feng, Boyuan Tong, Mengyu Wang, Haichen Yu, Yongqiang Mao, Hao Chang, Wenhui Diao, Peijin Wang, Yue Yu, Hanyang Peng, Yehong Zhang, Kun Fu, Xian Sun
    IEEE TPAMI, 2025 CCF-A [PDF]
  • [78] Centrality-guided Pre-training for Graph
    Bin Liang, Shiwei Chen, Lin Gui, Hui Wang, Yue Yu, Ruifeng Xu, Kam-Fai Wong
    ICLR 2025, Singapore - [PDF]
  • [79] CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
    Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu
    ACL 2025, Vienna, Austria CCF-A [PDF]
2024
  • [80] Optimus: Warming Serverless ML Inference via Inter-Function Model Transformation
    Zicong Hong, Jian Lin, Song Guo, Sifu Luo, Wuhui Chen, Roger Wattenhofer, Yue Yu
    EuroSys 2024, Athens, Greece CCF-A [PDF]
  • [81] Training and Serving System of Foundation Models: A Comprehensive Survey
    Jiahang Zhou, Yanyu Chen, Zicong Hong, Wuhui Chen, Yue Yu, Tao Zhang, Hui Wang, Chuanfu Zhang, Zibin Zheng
    IEEE OJCS, 2024 SCI [PDF]
  • [82] Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
    Xueyang Tang, Song Guo, Jingcai Guo, Jie Zhang, Yue Yu
    ICML 2024, Vienna, Austria CCF-A [PDF]
  • [83] SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC
    Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu
    ACL 2024, Bangkok, Thailand CCF-A [PDF]
2023
  • [84] A Survey on Scheduling Techniques in Computing and Network Convergence
    Shujiong Tang, Yue Yu, Hui Wang, Guiliang Wang, Wuhui Chen, Zenglin Xu, Song Guo, Wen Gao
    IEEE COMST, 2023 JCR-Q1 [PDF]
  • [85] Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training
    Hanyang Peng, Shuang Qin, Yue Yu, Jin Wang, Hui Wang, Ge Li
    NeurIPS 2023, New Orleans, USA CCF-A [PDF]
  • [86] Intelligence-Endogenous Management Platform for Computing and Network Convergence
    Zicong Hong, Xiaoyu Qiu, Jian Lin, Wuhui Chen, Yue Yu, Hui Wang, Song Guo, Wen Gao
    IEEE Network, 2023 JCR-Q1 [PDF]
  • [87] FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models
    Zhuo Zhang, Yuanhang Yang, Yong Dai, Qifan Wang, Yue Yu, Lizhen Qu, Zenglin Xu
    ACL 2023, Toronto, Canada CCF-A [PDF]
  • [88] Ginver: Generative Model Inversion Attacks Against Collaborative Inference
    Yupeng Yin, Xianglong Zhang, Huanle Zhang, Feng Li, Yue Yu, Xiuzhen Cheng, Pengfei Hu
    WWW 2023, Austin, USA CCF-A [PDF]
  • [89] Fine-grained Key-Value Memory Enhanced Predictor for Video Representation Learning
    Xiaojie Li, Jianlong Wu, Shaowei He, Kang Shuo, Yue Yu, Liqiang Nie, Min Zhang
    ACM MM 2023, Ottawa, Canada CCF-A [PDF]
  • [90] Mask Again: Masked Knowledge Distillation for Masked Video Modeling
    Xiaojie Li, Shaowei He, Jianlong Wu, Yue Yu, Liqiang Nie, Min Zhang
    ACM MM 2023, Ottawa, Canada CCF-A [PDF]
2021
  • [91] PanGu-a: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
    (集体作者)
    arXiv 2104.12369, 2021
2026
  • [92] GEPO: Group Expectation Policy Optimization for Stable Heterogeneous Reinforcement Learning
    Han Zhang, Ruibin Zheng, ZeXuan Yi, Zhuo Zhang, Hanyang Peng, Hui Wang, Jiayin Qi, Binxing Fang, Ruifeng Xu, Yue Yu
    ICLR 2026, Brazil - [PDF]
  • [93] Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
    Yong Chu, Xun Zhou, Zenglin Xu, Hui Wang, Yue Yu
    ICLR 2026, Brazil - [PDF]
  • [94] Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning
    Dexia Chen, Qianjie Zhu, Weibing Li, Yue Yu, Tong Zhang, Ruixuan Wang
    ICLR 2026, Brazil - [PDF]
  • [95] SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC
    Jinglong Luo, Zhuo Zhang, Yehong Zhang, Shiyu Liu, Ye Dong, Hui Wang, Yue Yu, Xun Zhou, Zenglin Xu
    ICLR 2026, Brazil - [PDF]
  • [96] Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts
    Qi Wang, Hanyang Peng, Yue Yu
    AAAI 2026, Singapore CCF-A [PDF]
2025
  • [97] SpecEM: Training-Free LLM Ensembling via Iterative Drafting, Verification, and Online Feedback
    Bo Lv, Nayu Liu, Chen Tang, Xin Liu, Yue Yu, Ping Luo
    NeurIPS 2025, San Diego, USA CCF-A [PDF]
  • [98] OBDD-NET: End-to-End Learning of Ordered Binary Decision Diagrams
    Junming Qiu, Rongzhen Ye, Weilin Luo, Kunxun Qi, Hai Wan, Yue Yu
    CIKM 2025, Seoul, Korea CCF-B [PDF]
  • [99] Correcting Large Language Model Behavior via Influence Function
    Han Zhang, Zhuo Zhang, Yi Zhang, Yuanzhao Zhai, Hanyang Peng, Yu Lei, Yue Yu, Hui Wang, Bin Liang, Lin Gui, Ruifeng Xu
    AAAI 2025, Pennsylvania, USA CCF-A [PDF]
  • [100] Preference-Strength-Aware Self-Improving Alignment with Generative Preference Models
    Yuanzhao Zhai, Zhuo Zhang, Cheng Yang, Kele Xu, Yue Yu, Wei Li, Hui Wang, Zenglin Xu, Dawei Feng, Bo Ding, Huaimin Wang
    SIGIR 2025, Padua, Italy - [PDF]
  • [101] COPR: Continual Human Preference Learning via Optimal Policy Regularization
    Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu
    ACL 2025, Vienna, Austria CCF-A [PDF]
  • [102] Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning
    Bo Lv, Nayu Liu, Yang Shen, Xin Liu, Ping Luo, Yue Yu
    ACL 2025, Vienna, Austria CCF-A [PDF]
2024
  • [103] At Which Training Stage Does Code Data Help LLMs Reasoning?
    Yingwei Ma, Yue Liu, Yue Yu, Yuanliang Zhang, Yu Jiang, Changjian Wang, Shanshan Li
    ICLR 2024, Vienna, Austria - [PDF]
  • [104] EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection
    Xin Mu, Yu Wang, Zhengan Huang, Junzuo Lai, Yehong Zhang, Hui Wang, Yue Yu
    AAAI 2024, Vancouver, Canada CCF-A [PDF]
  • [105] Rethinking the Evaluation of In-Context Learning for LLMs
    Guoxin Yu, Lemao Liu, Mo Yu, Yue Yu, Xiang Ao
    EMNLP 2024, Miami, USA CCF-A [PDF]
  • [106] Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring
    Jiewei Zhang, Song Guo, Peiran Dong, Jie Zhang, Ziming Liu, Yue Yu, Xiaoming Wu
    ICML 2024, Vienna, Austria CCF-A [PDF]
  • [107] URG: A Unified Ranking and Generation Method for Ensembling Language Models
    Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Ping Luo, Yue Yu
    ACL 2024, Bangkok, Thailand CCF-A [PDF]
  • [108] GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
    Xiaojie Li, Yibo Yang, Xiangtai Li, Jianlong Wu, Yue Yu, Bernard Ghanem, Min Zhang
    ECCV 2024, Milan, Italy CCF-A [PDF]
2023
  • [109] DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction
    Bo Lv, Xin Liu, Shaojie Dai, Nayu Liu, Fan Yang, Ping Luo, Yue Yu
    ACL 2023, Toronto, Canada CCF-A [PDF]
  • [110] Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing
    Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Yue Yu, Hui Wang, Zenglin Xu
    UAI 2023, Pittsburgh, USA CCF-B [PDF]
  • [111] Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery
    Xiaoqi Wang, Yingjie Cheng, Yaning Yang, Yue Yu, Fei Li, Shaoliang Peng
    Nature Machine Intelligence, 2023 - [PDF]
  • [112] Re-Thinking the Effectiveness of Batch Normalization and Beyond
    Hanyang Peng, Yue Yu, Shiqi Yu
    IEEE TPAMI, 2023 CCF-A [PDF]
2022
  • [113] NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
    Haibin Zheng, Zhiqing Chen, Tianyu Du, Xuhong Zhang, Yao Cheng, Shouling Ji, Jingyi Wang, Yue Yu, Jinyin Chen
    ICSE 2022, Pittsburgh, USA CCF-A [PDF]
2021
  • [114] Transductive Relation-Propagation with Decoupling Training for Few-Shot Learning
    Yuqing Ma, Shihao Bai, Wei Liu, Shuo Wang, Yue Yu, Xiao Bai, Xianglong Liu, Meng Wang
    IEEE TNNLS, 2021 CCF-B
  • [115] Detecting Adversarial Samples with Graph-Guided Testing
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