🍭 Recent Publications (2021-)

See my Google Scholar, DBLP, ORCID, and ResearchGate for the full publication list. ^†Equal contribution and ^♠Corresponding author for multi-first/corresponding author papers.

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Preprints & Working Papers

[w25r] A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations, with Li Li, Peilin Cai, Ryan A. Rossi, Franck Dernoncourt, Branislav Kveton, Junda Wu, Tong Yu, Lixin Song, Tiankai Yang, Yuehan Qin, Nesreen K. Ahmed, Samyadeep Basu, Subhojyoti Mukherjee, Ruiyi Zhang, Yuxiao Zhou, Zichao Wang, Yue Huang, Yu Wang, Xiangliang Zhang, Philip S. Yu, Xiyang Hu, Yue Zhao. Preprint.

[w25q] SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models, with Zixiang Xu, Yanbo Wang, Yue Huang, Jiayi Ye, Haomin Zhuang, Zirui Song, Lang Gao, Chenxi Wang, Zhaorun Chen, Yujun Zhou, Sixian Li, Wang Pan, Yue Zhao, Jieyu Zhao, Xiangliang Zhang, Xiuying Chen. Preprint.

[w25p] AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection, with Tiankai Yang, Junjun Liu, Wingchun Siu, Jiahang Wang, Zhuangzhuang Qian, Chanjuan Song, Cheng Cheng, Xiyang Hu, Yue Zhao. Preprint.

[w25o] GLIP-OOD: Zero-Shot Graph OOD Detection with Foundation Model, with Haoyan Xu, Zhengtao Yao, Xuzhi Zhang, Ziyi Wang, Langzhou He, Yushun Dong, Philip S. Yu, Mengyuan Li, Yue Zhao. Preprint.

[w25n] Graph Synthetic Out-of-Distribution Exposure with Large Language Models, with Haoyan Xu, Zhengtao Yao, Ziyi Wang, Zhan Cheng, Xiyang Hu, Mengyuan Li, Yue Zhao. Preprint.

[w25r] Doxing via the Lens: Revealing Privacy Leakage in Image Geolocation for Agentic Multi-Modal Large Reasoning Model, with Weidi Luo, Qiming Zhang, Tianyu Lu, Xiaogeng Liu, Yue Zhao, Zhen Xiang, Chaowei Xiao. Preprint.

[w25m] Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning, with Yuehan Qin, Shawn Li, Yi Nian, Xinyan Velocity Yu, Yue Zhao, Xuezhe Ma. Preprint.

[w25l] StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization, with Yiming Tang, Yi Fan, Chenxiao Yu, Tiankai Yang, Yue Zhao, Xiang Hu. Preprint.

[w25k] JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model, with Yi Nian, Shenzhe Zhu, Yuehan Qin, Li Li, Ziyi Wang, Chaowei Xiao, Yue Zhao. Preprint.

[w25i] DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning, with Chengxuan Qian, Shuo Xing, Shawn Li, Zhengzhong Tu. Preprint.

[w25h] Treble Counterfactual VLMs: A Causal Approach to Hallucination, with Li Li, Jiashu Qu, Yuxiao Zhou, Yuehan Qin, Tiankai Yang. Preprint.

[w25f] Can Multimodal LLMs Perform Time Series Anomaly Detection? with Xiongxiao Xu, Haoran Wang, Yueqing Liang, Philip S. Yu, Yue Zhao, Kai Shu.Preprint.

[w25e] A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments, with Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong. Preprint.

[w25d] On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective, with Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang, Jiayi Ye, Jiawen Shi, Qihui Zhang, many other authors, and Xiangliang Zhang. Preprint. See the Project Website.

[w25c] ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models, with Shixuan Li, Wei Yang, Peiyu Zhang, Xiongye Xiao, Defu Cao, Yuehan Qin, Xiaole Zhang, Yue Zhao, Paul Bogdan. Preprint.

[w24p] A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science, with Chenxiao Yu, Jinyi Ye, Yuangang Li, Zheng Li, Emilio Ferrara, Xiyang Hu. SSRN Top Download Papers for Decision Science. Preprint.

[w24o] Political-LLM: Large Language Models in Political Science, with Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Yushun Dong. Preprint.

[w24n] Personalized Multimodal Large Language Models: A Survey, with Junda Wu, Hanjia Lyu, Yu Xia, Zhehao Zhang, Joe Barrow, Ishita Kumar, Mehnoosh Mirtahebi, and others. Preprint.

[w24m] NLP-ADBench: NLP Anomaly Detection Benchmark, with Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu. Preprint.

[w24l] H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications, with Jiechao Gao, Yuangang Li, Brad Campbell. Preprint.

[w24k] DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration, with Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Tianfan Fu. Accepted to 2025 AAAI Workshop on Foundation Models for Biological Discoveries (FMs4Bio) Preprint.

[w24i] COOD: Concept-based Zero-shot OOD Detection, with Zhendong Liu, Yi Nian, Henry Peng Zou, Li Li, Xiyang Hu. Preprint.

[w24h] AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?, with Han Bao, Yue Huang, Yanbo Wang, Jiayi Ye, Xiangqi Wang, Xiuying Chen, Tianyi Zhou, Mohamed Elhoseiny, Xiangliang Zhang. Preprint.

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Conference Papers

• Secure On-Device Video OOD Detection Without Backpropagation .
  Li Li, Peilin Cai, Yuxiao Zhou, Zhiyu Ni, Renjie Liang, You Qin, Yi Nian, Zhengzhong Tu, Xiyang Hu, Yue Zhao.
  ICCV, 2025.
• Few-Shot Graph Out-of-Distribution Detection with LLMs .
  Haoyan Xu^†, Zhengtao Yao^†, Yushun Dong, Ziyi Wang, Ryan A. Rossi, Mengyuan Li, Yue Zhao.
  ECML PKDD, 2025.
• AD-LLM: Benchmarking Large Language Models for Anomaly Detection .
  Tiankai Yang^†, Yi Nian^†, Shawn Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Xiyang Hu, Ryan Rossi, Kaize Ding, Xia Hu, Yue Zhao.
  ACL (Findings), 2025.
• From Selection to Generation: A Survey of LLM-based Active Learning .
  Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley.
  ACL, 2025.
• A Survey on Model Extraction Attacks and Defenses for Large Language Models .
  Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong.
  KDD (Lecture-Style Tutorial Track), 2025.
• DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection .
  Shawn Li, Huixian Gong, Hao Dong, Tiankai Yang, Zhengzhong Tu, Yue Zhao^♠. Highlight Paper.
  CVPR, 2025.
• Edit Away and My Face Will Not Stay: Personal Biometric Defense against Malicious Generative Editing .
  Hanhui Wang, Yihua Zhang, Ruizheng Bai, Yue Zhao, Sijia Liu, Zhengzhong Tu.
  CVPR, 2025.
• TRUSTEVAL: A Dynamic Evaluation Toolkit on Trustworthiness of Generative Foundation Models .
  Yanbo Wang, Jiayi Ye, Siyuan Wu, Chujie Gao, Yue Huang, Xiuying Chen, Yue Zhao, Xiangliang Zhang.
  NAACL (Demo Track), 2025.
• MetaOOD: Automatic Selection of OOD Detection Models.
  Yuehan Qin^†, Yichi Zhang^†, Yi Nian^†, Xueying Ding, Yue Zhao^♠.
  KDD Workshop on Resource-Efficient Learning for Knowledge Discovery, 2024. Best Paper.
  ICLR, 2025.
• PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection.
  Sihan Chen, Zhuangzhuang Qian, Wingchun Siu, Xingcan Hu, Jiaqi Li, Shawn Li, Yuehan Qin, Tiankai Yang, Zhuo Xiao, Wanghao Ye, Yichi Zhang, Yushun Dong, Yue Zhao^♠.
  The Web Conference (Demo Track), 2025.
• MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities.
  Hao Dong, Yue Zhao, Eleni Chatzi, Olga Fink. Spotlight Paper.
  NeurIPS, 2024.
• Fast Unsupervised Deep Outlier Model Selection with Hypernetworks.
  Xueying Ding, Yue Zhao, Leman Akoglu
  KDD, 2024.
• TrustLLM: Trustworthiness in Large Language Models.
  Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, 50+ collaborative authors, Yue Zhao. Hugging Face Daily Papers.
  ICML, 2024.
• Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models.
  Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu. Oral Paper.
  ICML, 2024.
• Hyperparameter Optimization for Unsupervised Outlier Detection.
  Yue Zhao, Leman Akoglu.
  AutoML, 2024.
• Towards Reproducible, Automated, and Scalable Anomaly Detection.
  Yue Zhao.
  AAAI New Faculty Highlights, 2024.
• ADGym: Design Choices for Deep Anomaly Detection.
  Minqi Jiang^†, Chaochuan Hou^†, Ao Zheng^†, Songqiao Han, Hailiang Huang^♠, Qingsong Wen, Xiyang Hu^♠, Yue Zhao^♠.
  NeurIPS, 2023. .
• DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection.
  Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu.
  ECML/PKDD, 2023.
• Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks.
  Peng Xu^†, Lin Zhang^†, Xuanzhou Liu, Jiaqi Sun, Yue Zhao, Haiqin Yang, Bei Yu.
  ICML, 2023.
• TOD: GPU-accelerated Outlier Detection via Tensor Operations.
  Yue Zhao, George H. Chen, Zhihao Jia.
  VLDB, 2023.
• ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels.
  Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, Ahmed Awadallah.
  AAAI, 2023.
• ADBench: Anomaly Detection Benchmark.
  Songqiao Han^†, Xiyang Hu^†, Hailiang Huang^†, Minqi Jiang^†, Yue Zhao^†♠.
  NeurIPS, 2022.
• BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs.
  Kay Liu^†, Yingtong Dou^†, Yue Zhao^†, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu.
  NeurIPS, 2022.
• ELECT: Toward Unsupervised Outlier Model Selection.
  Yue Zhao, Sean Zhang, Leman Akoglu.
  ICDM, 2022.
• Automatic Unsupervised Outlier Model Selection.
  Yue Zhao, Ryan Rossi, Leman Akoglu.
  NeurIPS, 2021.
• Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development.
  Kexin Huang^†, Tianfan Fu^†, Wenhao Gao^†, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik.
  NeurIPS, 2021.
• Revisiting Time Series Outlier Detection: Definitions and Benchmarks.
  Kwei-Herng Lai^†, Daochen Zha^†, Junjie Xu, Yue Zhao, Guanchu Wang, Xia Hu.
  NeurIPS, 2021.
• SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection.
  Yue Zhao^†, Xiyang Hu^†, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu.
  MLSys, 2021.

Journal Papers

• LEGO-Learn: Label-Efficient Graph Open-Set Learning
  Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Kaize Ding^♠, Yue Zhao^♠
  Transactions on Machine Learning Research (TMLR), 2025.
• NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
  Hao Dong, Gaëtan Frusque, Yue Zhao, Eleni Chatzi, Olga Fink
  IEEE Transactions on Neural Networks and Learning Systems, 2024.
• Diffusion Models: A Comprehensive Survey of Methods and Applications.
  Ling Yang^†, Zhilong Zhang^†, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, Ming-Hsuan. 1000+ citations.
  ACM Computing Surveys, 2023.
• The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies.
  Martin Q. Ma^†, Yue Zhao^†, Xiaorong Zhang, Leman Akoglu.
  ACM SIGKDD Explorations Newsletter, 2023.
• Artificial Intelligence Foundation for Therapeutic Science.
  Kexin Huang^†, Tianfan Fu^†, Wenhao Gao^†, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik.
  Nature Chemical Biology, 2022.
• ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions.
  Zheng Li^†, Yue Zhao^†♠, Xiyang Hu, Nicola Botta, Cezar Ionescu, George H. Chen.
  TKDE, 2022.
• PyOD: A Python Toolbox for Scalable Outlier Detection.
  Yue Zhao, Zain Nasrullah, Zheng Li.
  JMLR, 2019.