📚 Publications

See my Google Scholar, DBLP, ORCID, and ResearchGate for the full publication list. Use Yue Zhao BibTeX (single file) for copy/paste. ^†Equal contribution and ^♠Corresponding author for multi-first/corresponding author papers.

This list emphasizes work on Auditable AI Systems across all four audit questions. Detection in data covers anomalies, outliers, and out-of-distribution inputs. Verification of output covers LLM evaluation, hallucination, and jailbreak detection. Auditing of action covers AI agent security, tool-call and MCP security, and audit trails. Optimization of effort covers agent efficiency, model routing, and the cost of defense training.

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Preprints & Working Papers

Show older preprints

2025

• TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs.
  Kay Liu, Yuwei Han, Haoyan Xu, Henry Peng Zou, Yue Zhao, Philip S. Yu
  arXiv preprint (2025)
• LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning.
  Haoyan Xu, Ruizhi Qian, Zhengtao Yao, Ziyi Liu, Li Li, Yuqi Li, Yanshu Li, Wenqing Zheng, Daniele Rosa, Daniel Barcklow, Senthil Kumar, Jieyu Zhao, Yue Zhao
  arXiv preprint (2025)
• A Systematic Study of Model Extraction Attacks on Graph Foundation Models.
  Haoyan Xu, Ruizhi Qian, Jiate Li, Yushun Dong, Minghao Lin, Hanson Yan, Zhengtao Yao, Qinghua Liu, Junhao Dong, Ruopeng Huang, Yue Zhao, Mengyuan Li
  arXiv preprint (2025)
• The Role of Computing Resources in Publishing Foundation Model Research.
  Yuexing Hao, Yue Huang, Haoran Zhang, Chenyang Zhao, Zhenwen Liang, Paul Pu Liang, Yue Zhao, Lichao Sun, Saleh Kalantari, Xiangliang Zhang, Marzyeh Ghassemi
  arXiv preprint (2025)
• Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs.
  Wang Wei, Tiankai Yang, Hongjie Chen, Yue Zhao, Franck Dernoncourt, Ryan A. Rossi, Hoda Eldardiry
  arXiv preprint (2025)
• Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization.
  Langzhou He, Junyou Zhu, Fangxin Wang, Junhua Liu, Haoyan Xu, Yue Zhao, Philip S. Yu, Qitian Wu
  arXiv preprint (2025)
• M3OOD: Automatic Selection of Multimodal OOD Detectors.
  Yuehan Qin, Li Li, Defu Cao, Tiankai Yang, Jiate Li, Yue Zhao
  arXiv preprint (2025)
• Graph Synthetic Out-of-Distribution Exposure with Large Language Models.
  Haoyan Xu, Zhengtao Yao, Ziyi Wang, Zhan Cheng, Xiyang Hu, Mengyuan Li, Yue Zhao
  arXiv preprint (2025)
• GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model.
  Haoyan Xu, Zhengtao Yao, Xuzhi Zhang, Ziyi Wang, Langzhou He, Yushun Dong, Philip S. Yu, Mengyuan Li, Yue Zhao
  arXiv preprint (2025)
• A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments.
  Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
  arXiv preprint (2025)
• ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models.
  Shixuan Li, Wei Yang, Peiyu Zhang, Xiongye Xiao, Defu Cao, Yuehan Qin, Xiaole Zhang, Yue Zhao, Paul Bogdan
  arXiv preprint (2025)

2024

• A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science.
  Chenxiao Yu, Jinyi Ye, Yuangang Li, Zheng Li, Emilio Ferrara, Xiyang Hu, Yue Zhao
  arXiv preprint (2024)
• Political-LLM: Large Language Models in Political Science.
  Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, and many others, Yue Zhao, Yushun Dong
  arXiv preprint (2024)
• Personalized Multimodal Large Language Models: A Survey.
  Junda Wu, Hanjia Lyu, Yu Xia, and many others, Yue Zhao, Jiebo Luo, Julian McAuley
  arXiv preprint (2024)

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Conference and Workshop Papers

• WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks.
  Prince Zizhuang Wang, Aojie Yuan, Haiyue Zhang, Xiyang Hu, Yue Zhao, and Shuli Jiang
  EMNLP Findings, 2026
• Modeling, Evaluating, and Enhancing Reasoning of Large Language Models in Complex Social Environments.
  Zixiang Xu, Yanbo Wang, Yue Huang, Haomin Zhuang, Yujun Zhou, Jiayi Ye, Sixian Li, Zirui Song, Lang Gao, Chenxi Wang, Zhaorun Chen, Wang Pan, Yue Zhao, Jieyu Zhao, Xiangliang Zhang, Xiuying Chen
  EMNLP Findings, 2026
• FinanceLLM: A Survey of Large Language Models in Finance.
  Ojas Nimase, Zhengao Li, Xinyu Wei, Jinglin Hu, Luojia Liu, Kangyi Zhao, Yue Zhao, Yushun Dong
  EMNLP Findings, 2026
• SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data.
  Wenjie Wang, Yue Huang, Zhengqing Yuan, Han Bao, Shiyi Du, Yuchen Ma, Yue Zhao, Yanfang Ye, Xiangliang Zhang
  EMNLP, 2026
• DOG-DPO: Dynamic Optimization in Geometry for Safety Alignment.
  Yi Nian, Tiankai Yang, Yudi Zhang, Qi Pan, Zelong Xu, Shenzhe Zhu, Qingqing Luan, Yue Huang, Xiangliang Zhang, Yue Zhao
  EMNLP Findings, 2026
• The Autonomy Tax: Defense Training Breaks LLM Agents.
  Shawn Li, Yue Zhao
  EMNLP, 2026
• FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing.
  Wang Wei, Hongzheng Yang, Tiankai Yang, Samyadeep Basu, Hongjie Chen, Yue Zhao, Franck Dernoncourt, Ryan A. Rossi, Hoda Eldardiry
  COLM, 2026
• Auditable Agents.
  Yi Nian, Aojie Yuan, Haiyue Zhang, Jiate Li, Li Li, Xiyang Hu, Hua Wei, Xiongye Xiao, Chaowei Xiao, and Yue Zhao
  ACM AI Leadership Summit, 2026 (condensed 4-page version); earlier version at ACL 2026 KnowFM Workshop
• AutoDavis: Automatic and Dynamic Evaluation Protocol of Large Vision-Language Models on Visual Question-Answering.
  Han Bao, Yue Huang, Yanbo Wang, Jiayi Ye, Xiangqi Wang, Xiuying Chen, Yue Zhao, Tianyi Zhou, Mohamed Elhoseiny, Xiangliang Zhang
  KDD D&B Track, 2026
• Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation.
  Zhisheng Qi, Utkarsh Sahu, Li Ma, Haoyu Han, Ryan Rossi, Franck Dernoncourt, Mahantesh Halappanavar, Nesreen Ahmed, Yushun Dong, Yue Zhao, Yu Zhang, Yu Wang
  KDD D&B Track, 2026
• SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution.
  Aojie Yuan, Yi Nian, Haiyue Zhang, Zijian Su, Yue Zhao.
  ICML 2026 Workshops (AI4GOOD and FAGEN)
• StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization.
  Yiming Tang, Yi Fan, Chenxiao Yu, Tiankai Yang, Yue Zhao, Xiyang Hu
  ICML Workshop on Trustworthy AI for Good (AI4GOOD), 2026
• "Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval.
  Jiate Li, Defu Cao, Li Li, Wei Yang, Yuehan Qin, Chenxiao Yu, Tiannuo Yang, Ryan A. Rossi, Yan Liu, Xiyang Hu, Yue Zhao
  ICML, 2026
• Defenses Against Prompt Attacks Learn Surface Heuristics.
  Shawn Li, Chenxiao Yu, Zhiyu Ni, Hao Li, Charith Peris, Chaowei Xiao, Yue Zhao.
  ACL, 2026
• CoAct: Co-Active LLM Preference Learning with Human-AI Synergy.
  Ruiyao Xu, Mihir Parmar, Tiankai Yang, Zhengyu Hu, Yue Zhao, Kaize Ding.
  ACL, 2026
• Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs.
  Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yan Liu, Yue Zhao, Xiyang Hu.
  ACL (Findings), 2026
• Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers.
  Yixuan Du, Chenxiao Yu, Haoyan Xu, Ziyi Wang, Yue Zhao, Xiyang Hu
  ACL Workshop on Towards Knowledgeable Foundation Models (KnowFM), 2026
• Can Multimodal LLMs Perform Time Series Anomaly Detection?.
  Xiongxiao Xu, Haoran Wang, Yueqing Liang, Philip S. Yu, Yue Zhao, Kai Shu.
  WWW, 2026
• A Survey on LLM-based Conversational User Simulation.
  Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Shawn Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi.
  EACL, 2026
• Charts Are Not Images: On the Challenges of Scientific Chart Editing.
  Shawn Li, Ryan Rossi, Sungchul Kim, Sunav Choudhary, Franck Dernoncourt, Puneet Mathur, Zhengzhong Tu, Yue Zhao.
  ICLR, 2026
• DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning.
  Chengxuan Qian, Shuo Xing, Shawn Li, Yue Zhao, Zhengzhong Tu.
  ICLR, 2026
• Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models.
  Weidi Luo, Tianyu Lu, Qiming Zhang, Xiaogeng Liu, Bin Hu, Yue Zhao, Jieyu Zhao, Song Gao, Patrick McDaniel, Zhen Xiang, Chaowei Xiao.
  ICLR, 2026
• TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models.
  Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang, Jiayi Ye, Jiawen Shi, Qihui Zhang, Yuan Li, Han Bao, Zhaoyi Liu, Tianrui Guan, Dongping Chen, Ruoxi Chen, Yue Zhao, many other authors, Xiangliang Zhang.
  ICLR, 2026
• Mitigating Hallucinations in Large Language Models via Causal Reasoning.
  Yuangang Li, Yiqing Shen, Yi Nian, Jiechao Gao, Ziyi Wang, Chenxiao Yu, Shawn Li, Jie Wang, Xiyang Hu, Yue Zhao.
  AAAI, 2026

Show older conference/workshop papers

• A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations.
  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.
  NeurIPS Workshop on Multi-Turn Interactions in Large Language Models (MTI-LLM), 2025
• DyFlow: Dynamic Workflow Framework for Agentic Reasoning.
  Yanbo Wang, Zixiang Xu, Yue Huang, Xiangqi Wang, Zirui Song, Lang Gao, Chenxi Wang, Xiangru Tang, Yue Zhao, Arman Cohan, Xiangliang Zhang, Xiuying Chen.
  NeurIPS, 2025
• SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models.
  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.
  NeurIPS Workshop on Socially Responsible and Trustworthy Foundation Models (ResponsibleFM), 2025
• AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection.
  Tiankai Yang, Junjun Liu, Michael Siu, Jiahang Wang, Zhuangzhuang Qian, Chanjuan Song, Cheng Cheng, Xiyang Hu, Yue Zhao.
  Findings of IJCNLP-AACL, 2025
• LLM-Empowered Patient-Provider Communication: A Data-Centric Survey From a Clinical Perspective.
  Ruosi Shao, Md Shamim Seraj, Kangyi Zhao, Yingtao Luo, Lincan Li, Bolin Shen, Averi Bates, Yue Zhao, Chongle Pan, Lisa Hightow-Weidman, Shayok Chakraborty, Yushun Dong.
  Findings of IJCNLP-AACL, 2025
• Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses.
  Bolin Shen, Eren Erman Ozguven, Yue Zhao, Guang Wang, Yiqun Xie, Yushun Dong.
  ACM SIGSPATIAL SpatialConnect Workshop, 2025
• NLP-ADBench: NLP Anomaly Detection Benchmark.
  Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu, Yue Zhao.
  EMNLP Findings, 2025
• Navigating Between Explainability and Extractability in Machine Learning as a Service.
  Ojas Nimase, Yue Zhao, Yushun Dong.
  IEEE ICDM BlueSky Track, 2025
• Treble Counterfactual VLMs: A Causal Approach to Hallucination.
  Shawn Li, Jiashu Qu, Yuxiao Zhou, Yuehan Qin, Tiankai Yang, Yue Zhao.
  EMNLP Findings, 2025
• TyphoFormer: Language-Augmented Transformer for Accurate Typhoon Track Forecasting.
  Lincan Li, Eren Erman Ozguven, Yue Zhao, Guang Wang, Yiqun Xie, Yushun Dong.
  ACM SIGSPATIAL, 2025
• JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model.
  Yi Nian^†, Shenzhe Zhu^†, Yuehan Qin, Shawn Li, Ziyi Wang, Chaowei Xiao, Yue Zhao.
  COLM, 2025
• Secure On-Device Video OOD Detection Without Backpropagation.
  Shawn 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
• 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
• AD-LLM: Benchmarking Large Language Models for Anomaly Detection.
  Tiankai Yang^†, Yi Nian^†, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. 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
• Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation.
  Zerui Xu, Fang Wu, Yue Zhao.
  ACM BCB, 2025
• DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection.
  Shawn Li, Huixian Gong, Hao Dong, Tiankai Yang, Zhengzhong Tu, Yue Zhao.
  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
• PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection.
  Sihan Chen, Zhuangzhuang Qian, Michael 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
• 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.
  ICLR, 2025
• DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration.
  Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Jieyu Zhao, Yingzhou Lu, Yue Zhao.
  AAAI Workshop on Foundation Models for Biological Discoveries (FMs4Bio), 2025
• MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities.
  Hao Dong, Yue Zhao, Eleni Chatzi, Olga Fink.
  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.
  ICML, 2024
• Towards Reproducible, Automated, and Scalable Anomaly Detection.
  Yue Zhao.
  AAAI New Faculty Highlights, 2024
• Hyperparameter Optimization for Unsupervised Outlier Detection.
  Yue Zhao, Leman Akoglu.
  AutoML, 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
• ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels.
  Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, Ahmed Awadallah.
  AAAI, 2023
• TOD: GPU-accelerated Outlier Detection via Tensor Operations.
  Yue Zhao, George H. Chen, Zhihao Jia.
  VLDB, 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
• Contrastive Attributed Network Anomaly Detection with Data Augmentation.
  Zhiming Xu, Xiao Huang, Yue Zhao, Yushun Dong, Jundong Li
  PAKDD, 2022
• Automatic Unsupervised Outlier Model Selection.
  Yue Zhao, Ryan Rossi, Leman Akoglu.
  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
• 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
• TODS: An Automated Time Series Outlier Detection System.
  Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Mingyang Wan, Diego Martinez, Xia Hu
  AAAI (Demo), 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
• COPOD: Copula-Based Outlier Detection.
  Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, Xiyang Hu
  IEEE ICDM, 2020
• SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources.
  Zheng Li, Yue Zhao, Jialin Fu
  IEEE ICDMW, 2020
• Combining Machine Learning Models using combo Library.
  Yue Zhao, Xuejian Wang, Cheng Cheng, Xueying Ding
  AAAI (Demo), 2020
• SUOD: Toward Scalable Unsupervised 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, Jiaze Sun, Leman Akoglu
  AAAI Workshop, 2020
• SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula.
  Colin Wan, Zheng Li, Alicia Guo, Yue Zhao
  AAAI Workshop, 2020
• A Data Denoising Approach to Optimize Functional Clustering of Single Cell RNA-sequencing Data.
  Changlin Wan, Dongya Jia, Yue Zhao, Wennan Chang, Sha Cao, Xiao Wang, Chi Zhang
  IEEE BIBM, 2020
• AutoAudit: Mining Accounting and Time-Evolving Graphs.
  Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre Jinghong Liang, Leman Akoglu, Vincent S. Tseng, Christos Faloutsos
  IEEE BigData, 2020
• DSR: An Accurate Single Image Super Resolution Approach for Various Degradations.
  Yiqun Mei, Yue Zhao, Wei Liang
  IEEE ICME, 2020
• LSCP: Locally Selective Combination in Parallel Outlier Ensembles.
  Yue Zhao, Zain Nasrullah, Maciej K. Hryniewicki, Zheng Li
  SDM, 2019
• Music Artist Classification with Convolutional Recurrent Neural Networks.
  Zain Nasrullah, Yue Zhao
  IJCNN, 2019
• DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles.
  Yue Zhao, Maciej K. Hryniewicki
  KDD Workshop, 2018
• Employee Turnover Prediction with Machine Learning: A Reliable Approach.
  Yue Zhao, Maciej K. Hryniewicki, Francesca Cheng, Boyang Fu, Xiaoyu Zhu
  IntelliSys, 2018
• XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning.
  Yue Zhao, Maciej K. Hryniewicki
  IJCNN, 2018
• An Empirical Study of Touch-based Authentication Methods on Smartwatches.
  Yue Zhao, Zhongtian Qiu, Yiqing Yang, Weiwei Li, Mingming Fan
  ISWC, 2017

Journal Papers

• Don’t Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning.
  Yuehan Qin, Shawn Li, Yi Nian, Xinyan Velocity Yu, Yue Zhao^♠, Xuezhe Ma^♠.
  Transactions on Machine Learning Research (TMLR), 2026

Show older 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
• 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
• 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 Yang.
  ACM Computing Surveys, 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.