🍭 Recent Publications (2021-)

See my Google Scholar, DBLP, ORCID, and ResearchGate for the full publication list. ^†Equal contribution; ^♠Corresponding author

---

Preprints & Working Papers

[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, Songtao 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.

[w24b] MetaOOD: Automatic Selection of OOD Detection Models, with Yuehan Qin, Yichi Zhang, Yi Nian, and Xueying Ding. Accepted to 2024 KDD Workshop on Resource-Efficient Learning for Knowledge Discovery. Best Paper. Preprint.

[w23i] NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation, with Hao Dong, Gaëtan Frusque, Eleni Chatzi, Olga Fink. Preprint.

[w23a] Weakly Supervised Anomaly Detection: A Survey, with Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu. Preprint.

---

Conference Papers

• 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

• 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.