Jaemin Yoo
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
32
Venues
13
Active years
2017–2026
Best venue rank
A*
Where they publish
Papers
32 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption. | Sunwoo Kim, Soo Yong Lee, Kyungho Kim, Hyunjin Hwang, Jaemin Yoo, Kijung Shin |
| 2026 | WWW | PULSE: Socially-Aware User Representation Modeling Toward Parameter-Efficient Graph Collaborative Filtering. | Doyun Choi, Cheonwoo Lee, Biniyam Aschalew Tolera, Taewook Ham, Chanyoung Park, Jaemin Yoo |
| 2026 | SIGIR | ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation. | Sunwoo Kim, Geon Lee, Kyungho Kim, Jaemin Yoo, Kijung Shin |
| 2025 | EMNLP | 'Hello, World!': Making GNNs Talk with LLMs. | Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, Kijung Shin |
| 2025 | ICML | Aggregation Buffer: Revisiting DropEdge with a New Parameter Block. | Dooho Lee, Myeong Kong, Sagad Hamid, Cheonwoo Lee, Jaemin Yoo |
| 2025 | KDD | Self-Tuning Self-Supervised Image Anomaly Detection. | Jaemin Yoo, Lingxiao Zhao, Leman Akoglu |
| 2025 | WWW | Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation. | Jin-Duk Park, Jaemin Yoo, Won-Yong Shin |
| 2025 | SDM | End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection. | Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefana Serral Asensio, Jaemin Yoo, Leman Akoglu |
| 2024 | ICLR | HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. | Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, Kijung Shin |
| 2024 | ICML | Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. | Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, Kijung Shin |
| 2024 | PAKDD | NETEFFECT: Discovery and Exploitation of Generalized Network Effects. | Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos |
| 2023 | ICML | Towards Deep Attention in Graph Neural Networks: Problems and Remedies. | Soo Yong Lee, Fanchen Bu, Jaemin Yoo, Kijung Shin |
| 2023 | KDD | Classification of Edge-dependent Labels of Nodes in Hypergraphs. | Minyoung Choe, Sunwoo Kim, Jaemin Yoo, Kijung Shin |
| 2023 | KDD | How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. | Sunwoo Kim, Fanchen Bu, Minyoung Choe, Jaemin Yoo, Kijung Shin |
| 2023 | KDD | Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. | Geon Lee, Jaemin Yoo, Kijung Shin |
| 2023 | KDD | Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining. | Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos |
| 2023 | WWW | Tutorials at The Web Conference 2023. | Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espn-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Kk-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne R. Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu |
| 2022 | CIKM | Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. | Geon Lee, Jaemin Yoo, Kijung Shin |
| 2022 | ICDM | Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators. | Sunwoo Kim, Minyoung Choe, Jaemin Yoo, Kijung Shin |
| 2022 | KDD | Accurate Node Feature Estimation with Structured Variational Graph Autoencoder. | Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, U Kang |
| 2022 | WWW | MiDaS: Representative Sampling from Real-world Hypergraphs. | Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin |
| 2022 | WWW | Model-Agnostic Augmentation for Accurate Graph Classification. | Jaemin Yoo, Sooyeon Shim, U Kang |
| 2022 | SDM | Transition Matrix Representation of Trees with Transposed Convolutions. | Jaemin Yoo, Lee Sael |
| 2021 | ICDM | Accurate Graph-Based PU Learning without Class Prior. | Jaemin Yoo, Junghun Kim, Hoyoung Yoon, Geonsoo Kim, Changwon Jang, U Kang |
| 2021 | KDD | Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts. | Jaemin Yoo, Yejun Soun, Yong-chan Park, U Kang |
| 2021 | PAKDD | Gaussian Soft Decision Trees for Interpretable Feature-Based Classification. | Jaemin Yoo, Lee Sael |
| 2021 | SDM | Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting. | Jaemin Yoo, U Kang |
| 2020 | WSDM | Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference. | Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, Marco Zaffalon |
| 2019 | ICDM | EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees. | Jaemin Yoo, Lee Sael |
| 2019 | IJCAI | Belief Propagation Network for Hard Inductive Semi-Supervised Learning. | Jaemin Yoo, Hyunsik Jeon, U Kang |
| 2018 | WSDM | Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs. | Saehan Jo, Jaemin Yoo, U Kang |
| 2017 | ICDM | Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks. | Jaemin Yoo, Saehan Jo, U Kang |