연구분야

딥 러닝 & 기계 학습

방대한 양의 데이터를 어떻게 학습할 수 있을까? 데이터 마이닝 연구실에서는 딥 러닝과 기계학습 모델, 알고리즘, 시스템 설계를 위한 연구를 진행한다. 주요 연구 주제는 다음과 같다.

모델, 알고리즘과 시스템:

  • 자동화 기계 학습 (AutoML): 자동화 학습 방법을 학습하는 AI를 디자인하고 개발한다.
  • 경량 기계학습: 빠르고 에너지 효율적인 모델을 구축하고자 한다.
  • 전이 기계학습: 라벨 데이터가 부족한 경우에도 고품질 모델을 구축하는 방법에 대해 연구한다.
  • 이상 탐지, 예측 및 단일 클래스 분류: 기계 고장 감지 및 예측 기능을 포함한 애플리케이션을 통해 문제의 이상 신호를 감지, 예측하기 위한 모델 및 알고리즘을 개발한다.
  • 센서를 이용한 물체 감지 및 분류: 다양한 센서 데이터를 이용하여 물체와 사람을 감지 및 구별하는 방법을 개발한다.
  • 질의 응답: 빅데이터와 딥 러닝 네트워크를 이용하여 질의 응답 기법을 개발한다.

연구실적

  • Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, and Marco Zaffalon, "Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference", The 13th ACM International WSDM Conference (WSDM) 2020, Houston, USA. [BIBTEX] [PDF]
  • Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang., "Knowledge Extraction with No Observable Data", Thirty-third Conference on Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada. [BIBTEX] [HOMEPAGE] [PDF]
  • Seung Cheol Park, Shuyu Wang, Hunjung Lim, and U Kang., "Curved-Voxel Clustering for Accurate Segmentation of 3D LIDAR Point Clouds with Real Time Performance", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019, Macao, China. [PDF]
  • Jamin Yoo, Hyunsik Jeon, and U Kang., "Belief Propagation Network for Hard Inductive Semi-supervised Learning", 28th International Joint Conference on Artificial Intelligence (IJCAI) 2019, Macao, China. [BIBTEX] [HOMEPAGE] [PDF]
  • Eunjeong Kang, Minsu Park, Peonggang Park, Namyong Park, Younglee Jung, U Kang, Hee Kyung Kang, Dong Ki Kim, Kwon Wook Joo, Yon Su Kim, Hyung Jin Yoon and Hajeong Lee, "Acute kidney injury predicts all-cause mortality in patients with cancer", Cancer Medicine (Cancer Medicine), 2019. [PDF]
  • Namyong Park, Eunjeong Kang, Minsu Park, Hajeong Lee, Hee-Gyung Kang, Hyung-Jin Yoon, and U Kang, "Predicting acute kidney injury in cancer patients using heterogeneous and irregular data", PLOS ONE, 2018. [PDF] [BIBTEX]
  • Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, and U Kang, "Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets", International Journal of Approximate Reasoning (IJAR), 2018. [PDF] [BIBTEX]
  • Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U Kang, "SIDE: Representation Learning in Signed Directed Networks", The Web Conference (WWW) 2018, Lyon, France. [BIBTEX] [HOMEPAGE] [PDF]
  • Saehan Jo, Jaemin Yoo, and U Kang, "Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs", 11th ACM International Conference on Web Search and Data Mining (WSDM) 2018, Los Angeles, CA, USA. [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Jaemin Yoo, Saehan Jo, and U Kang, "Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks", IEEE International Conference on Data Mining (ICDM) 2017, New Orleans, USA. [BIBTEX] [HOMEPAGE (CODE, DATA)] [PDF]
  • Kyung-Min Kim, Jinhong Jung, Jihee Ryu, Ha-Myung Park, Joseph P.Joohee, Seokwoo Jeong, U Kang, and Sung-Hyon Myaeng, "A New Question Answering Approach with Conceptual Graphs", Conférence en Recherche d’Information et Applications (CORIA) 2017, Marseille, France. [BIBTEX] [PDF]
  • Min-Hee Jang, Christos Faloutsos, Sang-Wook Kim, U Kang, and Jiwoon Ha, "PIN-TRUST: Fast Trust Propagation Exploiting Positive, Implicit, and Negative Information", ACM International Conference on Information and Knowledge Management (CIKM) 2016, Indianapolis, Indiana, USA. [BIBTEX] [PDF]
  • Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, and U Kang, "Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression", 23rd ACM International Conference on Information and Knowledge Management (CIKM) 2014, Shaghai, China [BIBTEX] [PDF] [SUPPLEMENTARY DOCUMENT]
  • U Kang, Martial Hebert, and Soonyong Park, "Fast and Scalable Approximate Spectral Graph Matching for Correspondence Problems", Information Sciences, 2013. [BIBTEX] [PDF]
  • U Kang, Hanghang Tong, and Jimeng Sun, "Fast Random Walk Graph Kernel", SIAM International Conference on Data Mining (SDM) 2012, Anaheim, California, USA. (acceptance rate 27 %) [BIBTEX] [PDF]
  • Danai Koutra, Tai-You Ke, U Kang, Duen Horng (Polo) Chau, Hsing-Kuo Kenneth Pao, and Christos Faloutsos, "Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms", European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2011, Athens, Greece. (acceptance rate 20.2 %) [BIBTEX] [PDF]
  • Robson L. F. Cordeiro, Caetano Traina Jr., Agma J. M. Traina, Julio Lopez, U Kang, and Christos Faloutsos, "Clustering Very Large Multi-dimensional Datasets with MapReduce", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2011, San Diego, CA, USA. (acceptance rate 17.5 %) [BIBTEX] [PDF]
  • U Kang, Duen Horng "Polo" Chau, and Christos Faloutsos, "Inference of Beliefs on Billion-Scale Graphs", Large-scale Data Mining: Theory and Applications (LDMTA) 2010, in conjunction with KDD 2010, Washington D.C., USA. [BIBTEX] [PDF]