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JUNGTAEK KIM Ph.D. Student |
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(* indicates equal contribution.)
Conference
- Juho Lee*, Yoonho Lee*, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, and Yee Whye Teh. Bootstrapping Neural Processes. In Advances in Neural Information Processing Systems 33 (NeurIPS-2020), Virtual-only conference, December 6-12, 2020.
- Jungtaek Kim and Seungjin Choi. On Local Optimizers of Acquisition Functions in Bayesian Optimization. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-2020), Virtual-only conference, September 14-18, 2020.
- 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.
Journal
- Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi. Bayesian Optimization with Approximate Set Kernels. Machine Learning, January 22, 2021. (To appear)
Workshop
- Jungtaek Kim, Hyunsoo Chung, Jinhwi Lee, Minsu Cho, and Jaesik Park. Combinatorial 3D Shape Generation via Sequential Assembly. Neural Information Processing Systems Workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng-2020), Virtual-only workshop, December 12, 2020.
- Jinhwi Lee*, Jungtaek Kim*, Hyunsoo Chung, Jaesik Park, and Minsu Cho. Fragment Relation Networks for Geometric Shape Assembly. Neural Information Processing Systems Workshop on Learning Meets Combinatorial Algorithms (LMCA-2020), Virtual-only workshop, December 12, 2020.
- Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi. Bayesian Optimization over Sets. International Conference on Machine Learning Workshop on Automated Machine Learning (AutoML-2019), Long Beach, California, USA, June 14, 2019.
- Minseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, and Seungjin Choi. TAEML: Task-Adaptive Ensemble of Meta-Learners. Neural Information Processing Systems 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 / International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence (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 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 Workshop on Automatic Machine Learning (AutoML-2016), New York, New York, USA, June 24, 2016.
Last updated: January 26, 2021