Research

Deep Learning & Machine Learning

How can we learn from massive amount of data? We work on designing models, algorithms, and systems for deep learning and machine learning. We focus on the following researches.

Models, Algorithms and Systems:

  • Autonomous machine learning (AutoML): we develop methods to design an AI that learns how to learn automatically.
  • Lightweight machine learning: we aim to build a fast and energy-efficient model.
  • Transferable machine learning: we aim to build a high quality model even when labeled instances are scarce.
  • Anomaly detection and prediction: we develop models and algorithms for finding anomalies and frauds.
  • Object detection and classification using sensors: we develop methods to detect objects and humans using data from various sensors.
  • Question answering: We develop question answering methods using big data and deep neural networks.

Publication

  • 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.
  • 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]