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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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HYDRA
A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series 
GLS Calibration
Generalized LS calibration for expensive computer models 
HiDe-prompt
Hierarchical Decomposition of Prompt-Based Continual Learning 
Multi-task CNN
An enhanced CNN by classification to make stocks prediction 
Solution for Drought
Water Sharing Strategy Solving Severe Drought in Five US States 
MC-ARIMA for Wordle Game
Forecasting the Difficulty of Words in the Wordle Game 
publications
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
Published in NeurIPS 2023 spotlight, 2023
Recommended citation: Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, Jun Zhu. (2023). Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality https://arxiv.org/abs/2310.07234
Dive into Time-Series Anomaly Detection: A Decade Review
Published in arXiv preprint arXiv:2412.20512, 2024
Recommended citation: Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, John Paparrizos. (2024). Dive into Time-Series Anomaly Detection: A Decade Review. https://arxiv.org/abs/2412.20512
VUS: effective and efficient accuracy measures for time-series anomaly detection
Published in The VLDB Journal, 2025
Recommended citation: Paul Boniol, Ashwin K. Krishna, Marine Bruel, Qinghua Liu, Mingyi Huang, Themis Palpanas, Ruey S. Tsay, Aaron Elmore, Michael J. Franklin, John Paparrizos. (2025). VUS: effective and efficient accuracy measures for time-series anomaly detection. https://link.springer.com/article/10.1007/s00778-025-00907-x
GlassboxAD: An Interactive System for Dissecting Hierarchical Time-Series Anomaly Detection
Published in SIGMOD Companion 2026, 2026
Recommended citation: Mingyi Huang, Qinghua Liu, Paul Boniol, John Paparrizos. (2026). GlassboxAD: An Interactive System for Dissecting Hierarchical Time-Series Anomaly Detection.
HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series
Published in Proceedings of the ACM on Management of Data (SIGMOD 2026), 2026
Recommended citation: Mingyi Huang, Qinghua Liu, Paul Boniol, John Paparrizos. (2026). HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series. https://doi.org/10.1145/3788853.3801594