An empirical study of touch-based authentication methods on smartwatches. - Yue Zhao 016; Zhongtian Qiu; Yiqing Yang; Weiwei Li; Mingming Fan 001 (ISWC, 2017)

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. - Yue Zhao 016; Maciej K. Hryniewicki (IJCNN, 2018)

Employee Turnover Prediction with Machine Learning: A Reliable Approach. - Yue Zhao 016; Maciej K. Hryniewicki; Francesca Cheng; Boyang Fu; Xiaoyu Zhu (IntelliSys (2), 2018)

Music Artist Classification with Convolutional Recurrent Neural Networks. - Zain Nasrullah; Yue Zhao 016 (IJCNN, 2019)

LSCP: Locally Selective Combination in Parallel Outlier Ensembles. - Yue Zhao 016; Zain Nasrullah; Maciej K. Hryniewicki; Zheng Li (SDM, 2019)

SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula. - Colin Wan; Zheng Li; Yue Zhao 016 (CoRR, 2019)

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles. - Yue Zhao 016; Maciej K. Hryniewicki (CoRR, 2019)

PyOD: A Python Toolbox for Scalable Outlier Detection. - Yue Zhao 016; Zain Nasrullah; Zheng Li (J. Mach. Learn. Res., 2019)

Combining Machine Learning Models Using combo Library. - Yue Zhao 016; Xuejian Wang; Cheng Cheng 001; Xueying Ding (AAAI, 2020)

A data denoising approach to optimize functional clustering of single cell RNA-sequencing data. - Changlin Wan; Dongya Jia; Yue Zhao 016; Wennan Chang; Sha Cao; Xiao Wang 012; Chi Zhang 021 (BIBM, 2020)

AutoAudit: Mining Accounting and Time-Evolving Graphs. - Meng-Chieh Lee; Yue Zhao 016; Aluna Wang; Pierre Jinghong Liang; Leman Akoglu; Vincent S. Tseng; Christos Faloutsos (IEEE BigData, 2020)

COPOD: Copula-Based Outlier Detection. - Zheng Li; Yue Zhao 016; Nicola Botta; Cezar Ionescu; Xiyang Hu (ICDM, 2020)

SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources. - Zheng Li; Yue Zhao 016; Jialin Fu (ICDM (Workshops), 2020)

Dsr: An Accurate Single Image Super Resolution Approach For Various Degradations. - Yiqun Mei; Yue Zhao 016; Wei Liang (ICME, 2020)

SUOD: Toward Scalable Unsupervised Outlier Detection. - Yue Zhao 016; Xueying Ding; Jianing Yang; Haoping Bai (CoRR, 2020)

SUOD: A Scalable Unsupervised Outlier Detection Framework. - Yue Zhao 016; Xiyang Hu; Cheng Cheng 001; Cong Wang; Cao Xiao; Yunlong Wang 001; Jimeng Sun 001; Leman Akoglu (CoRR, 2020)

Automating Outlier Detection via Meta-Learning. - Yue Zhao 016; Ryan A. Rossi; Leman Akoglu (CoRR, 2020)

TODS: An Automated Time Series Outlier Detection System. - Kwei-Herng Lai; Daochen Zha; Guanchu Wang; Junjie Xu; Yue Zhao 016; Devesh Kumar; Yile Chen 002; Purav Zumkhawaka; Minyang Wan; Diego Martinez; Xia Ben Hu (AAAI, 2021)

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection. - Yue Zhao 016; Xiyang Hu; Cheng Cheng 001; Cong Wang; Changlin Wan; Wen Wang; Jianing Yang; Haoping Bai; Zheng Li; Cao Xiao; Yunlong Wang 001; Zhi Qiao 007; et al. (MLSys, 2021)

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. - Kexin Huang; Tianfan Fu; Wenhao Gao 001; Yue Zhao 016; Yusuf H. Roohani; Jure Leskovec; Connor W. Coley; Cao Xiao; Jimeng Sun 001; Marinka Zitnik (NeurIPS Datasets and Benchmarks, 2021)

Revisiting Time Series Outlier Detection: Definitions and Benchmarks. - Kwei-Herng Lai; Daochen Zha; Junjie Xu; Yue Zhao 016; Guanchu Wang; Xia Ben Hu (NeurIPS Datasets and Benchmarks, 2021)

Automatic Unsupervised Outlier Model Selection. - Yue Zhao 016; Ryan A. Rossi; Leman Akoglu (NeurIPS, 2021)

PyHealth: A Python Library for Health Predictive Models. - Yue Zhao 016; Zhi Qiao 007; Cao Xiao; Lucas Glass; Jimeng Sun 001 (CoRR, 2021)

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics. - Kexin Huang; Tianfan Fu; Wenhao Gao 001; Yue Zhao 016; Yusuf H. Roohani; Jure Leskovec; Connor W. Coley; Cao Xiao; Jimeng Sun 001; Marinka Zitnik (CoRR, 2021)

A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice? - Martin Q. Ma; Yue Zhao 016; Xiaorong Zhang; Leman Akoglu (CoRR, 2021)

TOD: Tensor-based Outlier Detection. - Yue Zhao 016; George H. Chen; Zhihao Jia (CoRR, 2021)

Toward Unsupervised Outlier Model Selection. - Yue Zhao 016; Sean Zhang; Leman Akoglu (ICDM, 2022)

ADBench: Anomaly Detection Benchmark. - Songqiao Han; Xiyang Hu; Hailiang Huang; Minqi Jiang; Yue Zhao 016 (NeurIPS, 2022)

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; Lichao Sun 001; Jundong Li; et al. (NeurIPS, 2022)

Contrastive Attributed Network Anomaly Detection with Data Augmentation. - Zhiming Xu; Xiao Huang 001; Yue Zhao 016; Yushun Dong; Jundong Li (PAKDD (2), 2022)

PyGOD: A Python Library for Graph Outlier Detection. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; George H. Chen; Zhihao Jia; et al. (CoRR, 2022)

ADBench: Anomaly Detection Benchmark. - Songqiao Han; Xiyang Hu; Hailiang Huang; Mingqi Jiang; Yue Zhao 016 (CoRR, 2022)

Benchmarking Node Outlier Detection on Graphs. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; Lichao Sun 001; Jundong Li; et al. (CoRR, 2022)

Towards Unsupervised HPO for Outlier Detection. - Yue Zhao 016; Leman Akoglu (CoRR, 2022)

Diffusion Models: A Comprehensive Survey of Methods and Applications. - Ling Yang 006; Zhilong Zhang; Yang Song 011; Shenda Hong; Runsheng Xu; Yue Zhao 016; Yingxia Shao; Wentao Zhang 001; Ming-Hsuan Yang 001; Bin Cui 001 (CoRR, 2022)

TOD: GPU-accelerated Outlier Detection via Tensor Operations. - Yue Zhao 016; George H. Chen; Zhihao Jia (Proc. VLDB Endow., 2022)

ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels. - Yue Zhao 016; Guoqing Zheng; Subhabrata Mukherjee; Robert McCann; Ahmed Awadallah 001 (AAAI, 2023)

Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks. - Peng Xu; Lin Zhang; Xuanzhou Liu; Jiaqi Sun; Yue Zhao 016; Haiqin Yang; Bei Yu 001 (ICML, 2023)

ADGym: Design Choices for Deep Anomaly Detection. - Minqi Jiang; Chaochuan Hou; Ao Zheng; Songqiao Han; Hailiang Huang; Qingsong Wen; Xiyang Hu; Yue Zhao 016 (NeurIPS, 2023)

DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection. - Jaemin Yoo; Yue Zhao 016; Lingxiao Zhao; Leman Akoglu (ECML/PKDD (1), 2023)

Weakly Supervised Anomaly Detection: A Survey. - Minqi Jiang; Chaochuan Hou; Ao Zheng; Xiyang Hu; Songqiao Han; Hailiang Huang; Xiangnan He 001; Philip S. Yu; Yue Zhao 016 (CoRR, 2023)

Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks. - Peng Xu; Lin Zhang; Xuanzhou Liu; Jiaqi Sun; Yue Zhao 016; Haiqing Yang; Bei Yu 001 (CoRR, 2023)

GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization. - Yingzhou Lu; Minjie Shen; Yue Zhao 016; Chenhao Li; Fan Meng; Xiao Wang 012; David M. Herrington; Yue Wang; Tim Fu; Capucine Van Rechem (CoRR, 2023)

The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies. - Martin Q. Ma; Yue Zhao 016; Xiaorong Zhang; Leman Akoglu (SIGKDD Explor., 2023)

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions. - Zheng Li; Yue Zhao 016; Xiyang Hu; Nicola Botta; Cezar Ionescu; George H. Chen (IEEE Trans. Knowl. Data Eng., 2023)

Towards Reproducible, Automated, and Scalable Anomaly Detection. - Yue Zhao 016 (AAAI, 2024)

HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection. - Yue Zhao 016; Leman Akoglu (AutoML, 2024)

Position: TrustLLM: Trustworthiness in Large Language Models. - Yue Huang; Lichao Sun 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Yuan Li; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang; Xiner Li; Hanchi Sun; et al. (ICML, 2024)

Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models. - Songtao Liu; Hanjun Dai; Yue Zhao 016; Peng Liu (ICML, 2024)

Fast Unsupervised Deep Outlier Model Selection with Hypernetworks. - Xueying Ding; Yue Zhao 016; Leman Akoglu (KDD, 2024)

MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities. - Hao Dong 011; Yue Zhao 016; Eleni N. Chatzi; Olga Fink (NeurIPS, 2024)

TrustLLM: Trustworthiness in Large Language Models. - Lichao Sun 001; Yue Huang 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang 001; Xiner Li; Zhengliang Liu; Yixin Liu 002; et al. (CoRR, 2024)

Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation. - Zerui Xu; Fang Wu; Tianfan Fu; Yue Zhao 016 (CoRR, 2024)

LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Haoyan Xu; Kay Liu; Zhengtao Yao; Philip S. Yu; Kaize Ding; Yue Zhao 016 (CoRR, 2024)

Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models. - Chenxiao Yu; Zhaotian Weng; Yuangang Li 002; Zheng Li; Xiyang Hu; Yue Zhao 016 (CoRR, 2024)

COOD: Concept-based Zero-shot OOD Detection. - Zhendong Liu; Yi Nian; Henry Peng Zou; Li Li 006; Xiyang Hu; Yue Zhao 016 (CoRR, 2024)

DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration. - Sizhe Liu; Yizhou Lu; Siyu Chen; Xiyang Hu; Jieyu Zhao 001; Tianfan Fu; Yue Zhao 016 (CoRR, 2024)

Personalized Multimodal Large Language Models: A Survey. - Junda Wu; Hanjia Lyu; Yu Xia 007; Zhehao Zhang 001; Joe Barrow; Ishita Kumar; Mehrnoosh Mirtaheri; Hongjie Chen 003; Ryan A. Rossi; Franck Dernoncourt; Tong Yu 001; Ruiyi Zhang 002; et al. (CoRR, 2024)

NLP-ADBench: NLP Anomaly Detection Benchmark. - Yuangang Li 002; Jiaqi Li; Zhuo Xiao; Tiankai Yang 001; Yi Nian; Xiyang Hu; Yue Zhao 016 (CoRR, 2024)

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications. - Jiechao Gao; Yuangang Li 002; Yue Zhao 016; Brad Campbell (CoRR, 2024)

Political-LLM: Large Language Models in Political Science. - Lincan Li; Jiaqi Li; Catherine Chen 003; Fred Gui; Hongjia Yang; Chenxiao Yu; Zhengguang Wang; Jianing Cai; Junlong Aaron Zhou; Bolin Shen; Alex Qian; Weixin Chen; et al. (CoRR, 2024)

AD-LLM: Benchmarking Large Language Models for Anomaly Detection. - Tiankai Yang 001; Yi Nian; Shawn Li; Ruiyao Xu; Yuangang Li 002; Jiaqi Li; Zhuo Xiao; Xiyang Hu; Ryan A. Rossi; Kaize Ding; Xiaohu You 001; Yue Zhao 016 (CoRR, 2024)

A Large-scale Empirical Study on Large Language Models for Election Prediction. - Chenxiao Yu; Zhaotian Weng; Yuangang Li 002; Zheng Li; Xiyang Hu; Yue Zhao 016 (CoRR, 2024)

Diffusion Models: A Comprehensive Survey of Methods and Applications. - Ling Yang 006; Zhilong Zhang; Yang Song 011; Shenda Hong; Runsheng Xu; Yue Zhao 016; Wentao Zhang 001; Bin Cui 001; Ming-Hsuan Yang 001 (ACM Comput. Surv., 2024)

AD-LLM: Benchmarking Large Language Models for Anomaly Detection. - Tiankai Yang 001; Yi Nian; Li Li 006; Ruiyao Xu; Yuangang Li 002; Jiaqi Li; Zhuo Xiao; Xiyang Hu; Ryan A. Rossi; Kaize Ding; Xia Hu 001; Yue Zhao 016 (ACL (Findings), 2025)

From Selection to Generation: A Survey of LLM-based Active Learning. - Yu Xia 007; Subhojyoti Mukherjee; Zhouhang Xie; Junda Wu; Xintong Li 001; Ryan Aponte; Hanjia Lyu; Joe Barrow; Hongjie Chen 003; Franck Dernoncourt; Branislav Kveton; Tong Yu 001; et al. (ACL (1), 2025)

DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection. - Shawn Li; Huixian Gong; Hao Dong; Tiankai Yang 001; Zhengzhong Tu; Yue Zhao 016 (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 016; Sijia Liu; Zhengzhong Tu (CVPR, 2025)

MetaOOD: Automatic Selection of OOD Detection Models. - Yuehan Qin; Yichi Zhang; Yi Nian; Xueying Ding; Yue Zhao 016 (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 001; Zhuo Xiao; Wanghao Ye; Yichi Zhang; Yushun Dong; et al. (WWW (Companion Volume), 2025)

LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Haoyan Xu; Kay Liu; Zhengtao Yao; Philip S. Yu; Mengyuan Li; Kaize Ding; Yue Zhao 016 (Trans. Mach. Learn. Res., 2025)

ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models. - Shixuan Li; Wei Yang; Peiyu Zhang 002; Xiongye Xiao; Defu Cao; Yuehan Qin; Xiaole Zhang; Yue Zhao 016; Paul Bogdan (CoRR, 2025)

On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective. - Yue Huang 001; Chujie Gao; Siyuan Wu 001; Haoran Wang 005; Xiangqi Wang; Yujun Zhou 010; Yanbo Wang 005; Jiayi Ye; Jiawen Shi; Qihui Zhang; Yuan Li 032; Han Bao 003; et al. (CoRR, 2025)

A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments. - Kaixiang Zhao; Lincan Li; Kaize Ding; Neil Zhenqiang Gong; Yue Zhao 016; Yushun Dong (CoRR, 2025)

Can Multimodal LLMs Perform Time Series Anomaly Detection? - Xiongxiao Xu; Haoran Wang 005; Yueqing Liang; Philip S. Yu; Yue Zhao 016; Kai Shu (CoRR, 2025)

Secure On-Device Video OOD Detection Without Backpropagation. - Shawn Li; Peilin Cai; Yuxiao Zhou 006; Zhiyu Ni; Renjie Liang; You Qin; Yi Nian; Zhengzhong Tu; Xiyang Hu; Yue Zhao 016 (CoRR, 2025)

Treble Counterfactual VLMs: A Causal Approach to Hallucination. - Shawn Li; Jiashu Qu; Yuxiao Zhou 006; Yuehan Qin; Tiankai Yang 001; Yue Zhao 016 (CoRR, 2025)

Generative AI in Transportation Planning: A Survey. - Longchao Da; Tiejin Chen; Zhuoheng Li; Shreyas Bachiraju; Huaiyuan Yao; Li Li 006; Yushun Dong; Xiyang Hu; Zhengzhong Tu; Dongjie Wang; Yue Zhao 016; Xuanyu Zhou; et al. (CoRR, 2025)

DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning. - Chengxuan Qian; Shuo Xing; Shawn Li; Yue Zhao 016; Zhengzhong Tu (CoRR, 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 016 (CoRR, 2025)

Efficient Model Selection for Time Series Forecasting via LLMs. - Wang Wei; Tiankai Yang 001; Hongjie Chen 003; Ryan A. Rossi; Yue Zhao 016; Franck Dernoncourt; Hoda Eldardiry (CoRR, 2025)

JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model. - Yi Nian; Shenzhe Zhu; Yuehan Qin; Li Li 006; Ziyi Wang; Chaowei Xiao; Yue Zhao 016 (CoRR, 2025)

StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization. - Yiming Tang; Yi Fan; Chenxiao Yu; Tiankai Yang 001; Yue Zhao 016; Xiyang Hu (CoRR, 2025)

Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning. - Yuehan Qin; Shawn Li; Yi Nian; Xinyan Velocity Yu; Yue Zhao 016; Xuezhe Ma (CoRR, 2025)

Doxing via the Lens: Revealing Privacy Leakage in Image Geolocation for Agentic Multi-Modal Large Reasoning Model. - Weidi Luo; Qiming Zhang; Tianyu Lu; Xiaogeng Liu; Yue Zhao 016; Zhen Xiang; Chaowei Xiao (CoRR, 2025)

GLIP-OOD: Zero-Shot Graph OOD Detection with Foundation Model. - Haoyan Xu; Zhengtao Yao; Xuzhi Zhang; Ziyi Wang; Langzhou He; Yushun Dong; Philip S. Yu; Mengyuan Li; Yue Zhao 016 (CoRR, 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 016 (CoRR, 2025)

AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection. - Tiankai Yang 001; Junjun Liu; Wingchun Siu; Jiahang Wang; Zhuangzhuang Qian; Chanjuan Song; Cheng Cheng 001; Xiyang Hu; Yue Zhao 016 (CoRR, 2025)

A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations. - Li Li 006; Peilin Cai; Ryan A. Rossi; Franck Dernoncourt; Branislav Kveton; Junda Wu; Tong Yu 001; Linxin Song; Tiankai Yang 001; Yuehan Qin; Nesreen K. Ahmed; Samyadeep Basu; et al. (CoRR, 2025)

SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models. - Zixiang Xu; Yanbo Wang 005; Yue Huang 001; Jiayi Ye; Haomin Zhuang; Zirui Song; Lang Gao; Chenxi Wang; Zhaorun Chen; Yujun Zhou 010; Sixian Li; Wang Pan; et al. (CoRR, 2025)

TyphoFormer: Language-Augmented Transformer for Accurate Typhoon Track Forecasting. - Lincan Li; Eren Erman Ozguven; Yue Zhao 016; Guang Wang; Yiqun Xie; Yushun Dong (CoRR, 2025)

Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses. - Bolin Shen; Eren Erman Ozguven; Yue Zhao 016; Guang Wang; Yiqun Xie; Yushun Dong (CoRR, 2025)

A Survey on Model Extraction Attacks and Defenses for Large Language Models. - Kaixiang Zhao; Lincan Li; Kaize Ding; Neil Zhenqiang Gong; Yue Zhao 016; Yushun Dong (CoRR, 2025)

NNG-Mix: Improving Semi-Supervised Anomaly Detection With Pseudo-Anomaly Generation. - Hao Dong 011; Gaëtan Frusque; Yue Zhao 016; Eleni N. Chatzi; Olga Fink (IEEE Trans. Neural Networks Learn. Syst., 2025)

M3OOD: Automatic Selection of Multimodal OOD Detectors. - Yuehan Qin; Li Li 006; Defu Cao; Tiankai Yang 001; Yue Zhao 016 (CoRR, 2025)

Mitigating Hallucinations in Large Language Models via Causal Reasoning. - Yuangang Li 002; Yiqing Shen; Yi Nian; Jiechao Gao; Ziyi Wang; Chenxiao Yu; Shawn Li; Jie Wang; Xiyang Hu; Yue Zhao 016 (CoRR, 2025)

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives. - Kaixiang Zhao; Lincan Li; Kaize Ding; Neil Zhenqiang Gong; Yue Zhao 016; Yushun Dong (CoRR, 2025)