AEGIS: Toward Expert-in-the-loop Industrial Anomaly Detection
Dongmin Kim, Ye Seul Sim, Suhee Yoon, Sanghyu Yoon, Seungdong Yoa, Soonyoung Lee, Woohyung Lim, Association for the Advancement of Artificial Intelligence (AAAI), Demo Track, 2026 (28.45% acceptance rate)

ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection [site] [arxiv] [huggingface] [code]
Sanghyu Yoon*, Dongmin Kim*, Suhee Yoon, Ye Seul Sim, Seungdong Yoa, Hye-Seung Cho, Soonyoung Lee, Hankook Lee, Woohyung Lim,  arxiv preprint

Representation Space Augmentation for Effective Self-Supervised Learning on Tabular Data
Moonjung Eo, Kyungeun Lee, Hye-Seung Cho, Dongmin Kim, Ye Seul Sim, Woohyung Lim, Association for the Advancement of Artificial Intelligence (AAAI), 2025 (23.75% acceptance rate)

When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection    [arxiv] [code]
Dongmin Kim, Sunghyun Park, and Jaegul Choo
Association for the Advancement of Artificial Intelligence (AAAI), 2024 (23.75% acceptance rate).

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density  [arxiv]
Junwoo Park, Jungsoo Lee, Youngin Cho, Woncheol Shin, Dongmin Kim, Jaegul Choo, and Edward Choi
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023 (23.97% acceptance rate).

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting  [arxiv] [paper] [code]
Youngin Cho,* Daejin Kim,* Dongmin Kim, Mohammad Azam Khan, and Jaegul Choo  (*: equal contributions)
Conference on Neural Information Processing Systems (NeurIPS), 2022 (25.6% acceptance rate). 

Residual Correction in Real-Time Traffic Forecasting  [arxiv] [paper]
Daejin Kim,* Youngin Cho,* Dongmin Kim, Cheonbok Park, and Jaegul Choo (*: equal contributions)
ACM International Conference on Information and Knowledge Management (CIKM), 2022.