JUNGTAEK KIMPh.D. Student
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| ADDRESS | Machine Learning Group, Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang-si 37673, Gyeongsangbuk-do, Republic of Korea | |
| jtkim@postech.ac.kr; jtkim@postech.edu | ||
| PHONE | +82-54-279-2924 | |
| FAX | +82-54-279-2299 | |
| HOMEPAGE | http://mlg.postech.ac.kr/~jtkim |
EDUCATION
| Mar 01, 2015 - Present |
Ph.D. Course in Computer Science and Engineering, POSTECH (Advisor: Prof. Seungjin Choi and Prof. Minsu Cho) |
| Mar 01, 2010 - Feb 13, 2015 |
B.S. in Mechanical Engineering & Computer Science and Engineering, POSTECH |
| Mar 01, 2007 - Feb 28, 2010 |
High School Graduate at Hansung Science High School |
CURRENT RESEARCH INTERESTS
- Automation of Machine Learning
- Hyperparameter Optimization
- Bayesian Optimization
WORK EXPERIENCES
| Jan 02, 2018 - Apr 27, 2018 |
Research Engineering Intern, SigOpt Inc. / San Francisco, USA |
| Oct 30, 2017 - Dec 29, 2017 |
Research Intern, AITRICS / Seoul, Republic of Korea |
| Jul 25, 2017 - Oct 20, 2017 |
Research Intern, Samsung Electronics / Hwaseong, Repubic of Korea |
| Jan 01, 2016 - Jul 31, 2017 |
Advisor, XBrain Inc. / Pohang, Repubic of Korea |
| Jan 02, 2017 - Jan 26, 2017 |
Research Intern, Samsung Electronics / Hwaseong, Repubic of Korea |
| Aug 01, 2016 - Aug 26, 2016 |
Research Intern, Samsung Electronics / Hwaseong, Repubic of Korea |
PUBLICATIONS
Pre-print
- Minseop Park, Jungtaek Kim, Saehoon Kim, Yanbin Liu, and Seungjin Choi. MxML: Mixture of Meta-Learners for Few-Shot Classification. arXiv:1904.05658, April 11, 2019.
- Jungtaek Kim and Seungjin Choi. On Local Optimizers of Acquisition Functions in Bayesian Optimization. arXiv:1901.08350, January 24, 2019.
- Jungtaek Kim, Saehoon Kim, and Seungjin Choi. Learning to Warm-Start Bayesian Hyperparameter Optimization. arXiv:1710.06219, October 17, 2017.
Conference
- Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, and Yee Whye Teh. Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. In Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML-2019), Long Beach, California, USA, June 9-15, 2019.
- Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, and Seungjin Choi. Open Set Recognition by Regularizing Classifier with Fake Data Generated by Generative Adversarial Networks. In Proceedings of the Forty-Third IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2018), Calgary, Alberta, Canada, April 15-20, 2018.
- Jungtaek Kim and Seungjin Choi. Clustering-Guided GP-UCB for Bayesian Optimization. In Proceedings of the Forty-Third IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2018), Calgary, Alberta, Canada, April 15-20, 2018.
- Saehoon Kim, Jungtaek Kim, and Seungjin Choi. On the Optimal Bit Complexity of Circulant Binary Embedding. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018), New Orleans, Louisiana, USA, February 2-7, 2018.
Workshop
- Minseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, and Seungjin Choi. TAEML: Task-Adaptive Ensemble of Meta-Learners. Neural Information Processing Systems (NeurIPS) Workshop on Meta-Learning (MetaLearn-2018), Montreal, Quebec, Canada, December 8, 2018.
- Jungtaek Kim and Seungjin Choi. Automated Machine Learning for Soft Voting in an Ensemble of Tree-based Classifiers. International Workshop on Automatic Machine Learning at International Conference on Machine Learning (ICML) / International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence (IJCAI-ECAI) (AutoML-2018), Stockholm, Sweden, July 14, 2018.
- Jungtaek Kim, Saehoon Kim, and Seungjin Choi. Learning to Transfer Initializations for Bayesian Hyperparameter Optimization. Neural Information Processing Systems (NeurIPS) Workshop on Bayesian Optimization (BayesOpt-2017), Long Beach, California, USA, December 9, 2017.
- Jungtaek Kim, Jongheon Jeong, and Seungjin Choi. AutoML Challenge: AutoML Framework Using Random Space Partitioning Optimizer. International Conference on Machine Learning (ICML) Workshop on Automatic Machine Learning (AutoML-2016), New York, New York, USA, June 24, 2016. (Special track, 3-page)
AWARDS & HONOR
| Jun 09, 2019 | ICML Travel Award for ICML-2019 |
| Jun 06, 2018 | 2nd place in AutoML Challenge 2018 (PAKDD-2018 Data Competition) |
| Apr 15, 2018 | IEEE Signal Processing Society Travel Grant for ICASSP-2018 |
| Nov 18, 2016 | Best paper runner-up award for LG U+ in 2016 Fall Conference of Korea Business Intelligence Data Mining Society |
| Jun 24, 2016 | 3rd place in AutoML5 phase of AutoML Challenge |
| Jul 03, 2014 | Software Maestro (organized by Korea government (Ministry of Science, ICT and Future Planning & National IT Promotion Agency)) |
| Mar 02, 2010 | Presidential Science Scholarship |
PROFESSIONAL SERVICES
- Reviewer: AutoML-2019, ACML-2019, UAI-2019, ICML-2019, IJCNN-2019, AutoML-2018, ACML-2018, IJCNN-2018, IJCNN-2017
- Invited talks and non-peer reviewed presentations
TEACHING
Teaching Assistant
- Artificial Intelligence (Spring 2019), Probabilistic Graphical Models (Fall 2018), Machine Learning Course for Industries (SK Hynix (Fall 2018), Samsung Electronics Device Solutions Business (Fall 2017, Summer 2017), POSCO Group (Summer 2017, Spring 2017)), Automata & Formal Languages (Fall 2016), Machine Learning (Spring 2016)
ARCHIVES
- Curriculum Vitae
- Papers
- Research Topics
- Articles
- Pictures
- Assets
ELSEWHERE
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Last updated: May 10, 2019