JUNGTAEK KIM

Postdoctoral Associate
at the University of Pittsburgh

jungtaek.kim [at] pitt [dot] edu

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PUBLICATIONS: COMPLETE LIST · PUBLICATIONS: CONFERENCE / JOURNAL / WORKSHOP


(* and + indicate equal contribution by representing co-first and co-corresponding authors, respectively.)

Conference

  1. Jungtaek Kim, Seungjin Choi+, and Minsu Cho+. Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes. In Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI-2022), Eindhoven, the Netherlands, August 1-5, 2022.
  2. Jinhwi Lee*, Jungtaek Kim*, Hyunsoo Chung, Jaesik Park, and Minsu Cho. Learning to Assemble Geometric Shapes. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-2022), Vienna, Austria, July 23-29, 2022.
  3. Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, and Graham W. Taylor. On Evaluation Metrics for Graph Generative Models. In Proceedings of the Tenth International Conference on Learning Representations (ICLR-2022), Virtual, April 25-29, 2022.
  4. Jungtaek Kim and Seungjin Choi. On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization. In Proceedings of the Twenty-Fifth International Conference on Artificial Intelligence and Statistics (AISTATS-2022), Virtual, March 28-30, 2022.
  5. Hyunsoo Chung*, Jungtaek Kim*, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, and Minsu Cho. Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. In Advances in Neural Information Processing Systems 34 (NeurIPS-2021), Virtual, December 6-14, 2021.
  6. 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, December 6-12, 2020.
  7. 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, September 14-18, 2020.
  8. 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.
  9. 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.
  10. 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.
  11. 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

  1. Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi. Bayesian Optimization with Approximate Set Kernels. Machine Learning, vol. 110, no. 5, pp. 857-879, 2021.

Workshop

  1. Jungtaek Kim, Hyunsoo Chung, Jinhwi Lee, Minsu Cho, and Jaesik Park. Combinatorial 3D Shape Generation via Sequential Assembly. NeurIPS Workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng-2020), Virtual, December 12, 2020.
  2. Jinhwi Lee*, Jungtaek Kim*, Hyunsoo Chung, Jaesik Park, and Minsu Cho. Fragment Relation Networks for Geometric Shape Assembly. NeurIPS Workshop on Learning Meets Combinatorial Algorithms (LMCA-2020), Virtual, December 12, 2020.
  3. Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi. Bayesian Optimization over Sets. ICML Workshop on Automated Machine Learning (AutoML-2019), Long Beach, California, USA, June 14, 2019.
  4. Minseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, and Seungjin Choi. TAEML: Task-Adaptive Ensemble of Meta-Learners. NeurIPS Workshop on Meta-Learning (MetaLearn-2018), Montreal, Quebec, Canada, December 8, 2018.
  5. Jungtaek Kim and Seungjin Choi. Automated Machine Learning for Soft Voting in an Ensemble of Tree-based Classifiers. ICML Workshop on Automatic Machine Learning (AutoML-2018), Stockholm, Sweden, July 14, 2018.
  6. Jungtaek Kim, Saehoon Kim, and Seungjin Choi. Learning to Transfer Initializations for Bayesian Hyperparameter Optimization. NeurIPS Workshop on Bayesian Optimization (BayesOpt-2017), Long Beach, California, USA, December 9, 2017.
  7. Jungtaek Kim, Jongheon Jeong, and Seungjin Choi. AutoML Challenge: AutoML Framework Using Random Space Partitioning Optimizer. ICML Workshop on Automatic Machine Learning (AutoML-2016), New York, New York, USA, June 24, 2016.

Last updated: September 2, 2022