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ORCIDJaemin Yoo
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2020 – today
- 2024
[j5]Minyoung Choe
, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin:
Representative and Back-In-Time Sampling from Real-world Hypergraphs. ACM Trans. Knowl. Discov. Data 18(6): 156:1-156:48 (2024)- [c28]Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, Kijung Shin:
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. ICLR 2024 - [c27]Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, Kijung Shin:
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. ICML 2024 - [c26]Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos:
NETEFFECT: Discovery and Exploitation of Generalized Network Effects. PAKDD (1) 2024: 299-312 - [i20]Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, Kijung Shin:
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. CoRR abs/2402.04621 (2024) - [i19]Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, Kijung Shin:
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. CoRR abs/2404.00638 (2024) - [i18]Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu:
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection. CoRR abs/2404.02865 (2024) - [i17]Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, Kijung Shin:
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy. CoRR abs/2410.20366 (2024) - 2023
- [j4]Sunwoo Kim, Minyoung Choe, Jaemin Yoo, Kijung Shin:
Reciprocity in directed hypergraphs: measures, findings, and generators. Data Min. Knowl. Discov. 37(6): 2330-2388 (2023) - [j3]Jaemin Yoo, Tiancheng Zhao, Leman Akoglu:
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success. Trans. Mach. Learn. Res. 2023 (2023) - [c25]Leman Akoglu, Jaemin Yoo:
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities. IEEE Big Data 2023: 1047-1051 - [c24]Soo Yong Lee, Fanchen Bu, Jaemin Yoo, Kijung Shin:
Towards Deep Attention in Graph Neural Networks: Problems and Remedies. ICML 2023: 18774-18795 - [c23]Minyoung Choe, Sunwoo Kim, Jaemin Yoo, Kijung Shin:
Classification of Edge-dependent Labels of Nodes in Hypergraphs. KDD 2023: 298-309 - [c22]Sunwoo Kim, Fanchen Bu, Minyoung Choe, Jaemin Yoo, Kijung Shin:
How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. KDD 2023: 1132-1143 - [c21]Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos:
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining. KDD 2023: 3128-3139 - [c20]Geon Lee, Jaemin Yoo, Kijung Shin:
Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. KDD 2023: 5811-5812 - [c19]Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu:
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection. ECML/PKDD (1) 2023: 254-269 - [c18]Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-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 Küçük-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 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:
Tutorials at The Web Conference 2023. WWW (Companion Volume) 2023: 648-658 - [i16]Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos:
UltraProp: Principled and Explainable Propagation on Large Graphs. CoRR abs/2301.00270 (2023) - [i15]Sunwoo Kim, Fanchen Bu, Minyoung Choe, Jaemin Yoo, Kijung Shin:
How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. CoRR abs/2306.02358 (2023) - [i14]Soo Yong Lee, Fanchen Bu, Jaemin Yoo, Kijung Shin:
Towards Deep Attention in Graph Neural Networks: Problems and Remedies. CoRR abs/2306.02376 (2023) - [i13]Minyoung Choe, Sunwoo Kim, Jaemin Yoo, Kijung Shin:
Classification of Edge-dependent Labels of Nodes in Hypergraphs. CoRR abs/2306.03032 (2023) - [i12]Jaemin Yoo, Lingxiao Zhao, Leman Akoglu:
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection. CoRR abs/2306.12033 (2023) - [i11]Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu:
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection. CoRR abs/2307.06534 (2023) - [i10]Leman Akoglu, Jaemin Yoo:
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities. CoRR abs/2308.14380 (2023) - 2022
- [j2]Jaemin Yoo, Junghun Kim, Hoyoung Yoon, Geonsoo Kim, Changwon Jang, U Kang:
Graph-based PU learning for binary and multiclass classification without class prior. Knowl. Inf. Syst. 64(8): 2141-2169 (2022) - [c17]Yejun Soun, Jaemin Yoo, Minyong Cho, Jihyeong Jeon, U Kang:
Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets. IEEE Big Data 2022: 1691-1700 - [c16]Geon Lee, Jaemin Yoo, Kijung Shin:
Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. CIKM 2022: 5144-5147 - [c15]Sunwoo Kim, Minyoung Choe, Jaemin Yoo, Kijung Shin:
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators. ICDM 2022: 1005-1010 - [c14]Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, U Kang:
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder. KDD 2022: 2336-2346 - [c13]Jaemin Yoo, Lee Sael:
Transition Matrix Representation of Trees with Transposed Convolutions. SDM 2022: 154-162 - [c12]Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin:
MiDaS: Representative Sampling from Real-world Hypergraphs. WWW 2022: 1080-1092 - [c11]Jaemin Yoo, Sooyeon Shim, U Kang:
Model-Agnostic Augmentation for Accurate Graph Classification. WWW 2022: 1281-1291 - [i9]Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin:
MiDaS: Representative Sampling from Real-world Hypergraphs. CoRR abs/2202.01587 (2022) - [i8]Jaemin Yoo, Sooyeon Shim, U Kang:
Model-Agnostic Augmentation for Accurate Graph Classification. CoRR abs/2202.10107 (2022) - [i7]Jaemin Yoo, Lee Sael:
Transition Matrix Representation of Trees with Transposed Convolutions. CoRR abs/2202.10677 (2022) - [i6]Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, U Kang:
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder. CoRR abs/2206.04516 (2022) - [i5]Jaemin Yoo, Tiancheng Zhao, Leman Akoglu:
Understanding the Effect of Data Augmentation in Self-supervised Anomaly Detection. CoRR abs/2208.07734 (2022) - [i4]Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos:
SlenderGNN: Accurate, Robust, and Interpretable GNN, and the Reasons for its Success. CoRR abs/2210.04081 (2022) - [i3]Sunwoo Kim, Minyoung Choe, Jaemin Yoo, Kijung Shin:
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators. CoRR abs/2210.05328 (2022) - 2021
- [c10]Jaemin Yoo, Junghun Kim, Hoyoung Yoon, Geonsoo Kim, Changwon Jang, U Kang:
Accurate Graph-Based PU Learning without Class Prior. ICDM 2021: 827-836 - [c9]Jaemin Yoo, Yejun Soun, Yong-chan Park, U Kang:
Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts. KDD 2021: 2037-2045 - [c8]Jaemin Yoo, Lee Sael:
Gaussian Soft Decision Trees for Interpretable Feature-Based Classification. PAKDD (2) 2021: 143-155 - [c7]Jaemin Yoo, U Kang:
Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting. SDM 2021: 531-539 - 2020
- [c6]Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, Marco Zaffalon:
Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference. WSDM 2020: 708-716 - [i2]Jinhong Jung, Jaemin Yoo, U Kang:
Signed Graph Diffusion Network. CoRR abs/2012.14191 (2020)
2010 – 2019
- 2019
- [c5]Jaemin Yoo, Lee Sael:
EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees. ICDM 2019: 1438-1443 - [c4]Jaemin Yoo, Hyunsik Jeon, U Kang:
Belief Propagation Network for Hard Inductive Semi-Supervised Learning. IJCAI 2019: 4178-4184 - [c3]Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang:
Knowledge Extraction with No Observable Data. NeurIPS 2019: 2701-2710 - 2018
- [j1]Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, U Kang:
Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. Int. J. Approx. Reason. 95: 152-166 (2018) - [c2]Saehan Jo, Jaemin Yoo, U Kang:
Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs. WSDM 2018: 297-305 - [i1]Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, U Kang:
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets. CoRR abs/1802.02468 (2018) - 2017
- [c1]Jaemin Yoo, Saehan Jo, U Kang:
Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks. ICDM 2017: 595-604
Coauthor Index
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last updated on 2024-12-01 01:13 CET by the dblp team
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