Posts by Collection

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

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