🍭 Recent Publications (2021-)
See my Google Scholar,
DBLP,
ORCID,
and ResearchGate for the full publication list.
†Equal contribution; ♠Corresponding author
Preprints & Working Papers
[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.
[w25b] From Selection to Generation: A Survey of LLM-based Active Learning, with 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. Preprint.
[w25a] AD-LLM: Benchmarking Large Language Models for Anomaly Detection, with Tiankai Yang, Yi Nian, Shawn Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Xiyang Hu, Ryan Rossi, Kaize Ding, Xia Hu. 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, Hongjie Chen, Ryan A. Rossi, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Namyong Park, Sungchul Kim, Huanrui Yang, Subrata Mitra, Zhengmian Hu, Nedim Lipka, Jiebo Luo, Julian McAuley.
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.
[w24j] Edit Away and My Face Will Not Stay: Personal Biometric Defense against Malicious Generative Editing, with Hanhui Wang, Yihua Zhang, Ruizheng Bai, Sijia Liu, Zhengzhong Tu. Preprint.
[w24i] COOD: Concept-based Zero-shot OOD Detection, with Zhendong Liu, Yi Nian, Henry Peng Zou, Li Li, Xiyang Hu. Preprint.
[w24h] DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection, with Shawn Li, Huixian Gong, Hao Dong, Tiankai Yang, Zhengzhong Tu. Preprint.
[w24g] LEGO-Learn: Label-Efficient Graph Open-Set Learning, with Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Kaize Ding. Preprint.
[w24f] Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models, with Chenxiao Yu, Zhaotian Weng, Zheng Li, Xiyang Hu. SSRN Top Download Papers for Decision Science. Preprint.
[w24e] DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning, with Jiaqing Xie and Tianfan Fu. Accepted to 2024 NeurIPS Workshop on AI for New Drug Modalities. Spotlight Paper. Preprint.
[w24d] Artificial Intelligence-Aided Digital Twin Design: A Systematic Review, with Nan Hao, Yuangang Li, Kecheng Liu, Yingzhou Lu, Bohao Xu, Chenhao Li, Jintai Chen, Ling Yue, Tianfan Fu, Xiyang Hu, Xiao Wang. Preprint.
[w24c] GKAN: Graph Kolmogorov-Arnold Networks, with Mehrdad Kiamari, Mohammad Kiamari, Bhaskar Krishnamachari. Preprint.
Conference Papers
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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.
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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.
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MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities.
Hao Dong, Yue Zhao, Eleni Chatzi, Olga Fink. Spotlight Paper.
NeurIPS, 2024.
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Fast Unsupervised Deep Outlier Model Selection with Hypernetworks.
Xueying Ding, Yue Zhao, Leman Akoglu
KDD, 2024.
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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.
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Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models.
Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu. Oral Paper.
ICML, 2024.
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Hyperparameter Optimization for Unsupervised Outlier Detection.
Yue Zhao, Leman Akoglu.
AutoML, 2024.
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Towards Reproducible, Automated, and Scalable Anomaly Detection.
Yue Zhao.
AAAI New Faculty Highlights, 2024.
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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.
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DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection.
Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu.
ECML/PKDD, 2023.
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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.
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TOD: GPU-accelerated Outlier Detection via Tensor Operations.
Yue Zhao, George H. Chen, Zhihao Jia.
VLDB, 2023.
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ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels.
Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, Ahmed Awadallah.
AAAI, 2023.
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ADBench: Anomaly Detection Benchmark.
Songqiao Han†, Xiyang Hu†, Hailiang Huang†, Minqi Jiang†, Yue Zhao†♠.
NeurIPS, 2022.
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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.
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ELECT: Toward Unsupervised Outlier Model Selection.
Yue Zhao, Sean Zhang, Leman Akoglu.
ICDM, 2022.
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Automatic Unsupervised Outlier Model Selection.
Yue Zhao, Ryan Rossi, Leman Akoglu.
NeurIPS, 2021.
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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.
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Revisiting Time Series Outlier Detection: Definitions and Benchmarks.
Kwei-Herng Lai†, Daochen Zha†, Junjie Xu, Yue Zhao, Guanchu Wang, Xia Hu.
NeurIPS, 2021.
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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
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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 (Accepted).
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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.
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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.
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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.
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ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions.
Zheng Li†, Yue Zhao†♠, Xiyang Hu, Nicola Botta, Cezar Ionescu, George H. Chen.
TKDE, 2022.
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PyOD: A Python Toolbox for Scalable Outlier Detection.
Yue Zhao, Zain Nasrullah, Zheng Li.
JMLR, 2019.