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ORCIDJungtaek Kim 0001
Person information
- affiliation: University of Pittsburgh, PA, USA
- affiliation (former): Pohang University of Science and Technology (POSTECH), Department of Computer Science and Engineering, South Korea
Other persons with the same name
- Jungtaek Kim (aka: Jung-Taek Kim) — disambiguation page
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2020 – today
- 2023
[j2]Jungtaek Kim
, Seungjin Choi:
BayesO: A Bayesian optimization framework in Python. J. Open Source Softw. 8(90): 5320 (2023)- [c13]Jungtaek Kim, Mingxuan Li, Oliver Hinder, Paul W. Leu:
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations. NeurIPS 2023 - [c12]Tackgeun You, Mijeong Kim, Jungtaek Kim, Bohyung Han:
Generative Neural Fields by Mixtures of Neural Implicit Functions. NeurIPS 2023 - [i18]Jungtaek Kim:
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning. CoRR abs/2305.15612 (2023) - [i17]Jungtaek Kim, Jeongbeen Yoon, Minsu Cho:
Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions. CoRR abs/2310.07174 (2023) - [i16]Jungtaek Kim, Mingxuan Li, Oliver Hinder, Paul W. Leu:
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations. CoRR abs/2310.19053 (2023) - [i15]Tackgeun You, Mijeong Kim, Jungtaek Kim, Bohyung Han:
Generative Neural Fields by Mixtures of Neural Implicit Functions. CoRR abs/2310.19464 (2023) - 2022
- [c11]Jungtaek Kim, Seungjin Choi:
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization. AISTATS 2022: 4359-4375 - [c10]Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham W. Taylor:
On Evaluation Metrics for Graph Generative Models. ICLR 2022 - [c9]Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, Minsu Cho:
Learning to Assemble Geometric Shapes. IJCAI 2022: 1046-1052 - [c8]Jungtaek Kim, Seungjin Choi, Minsu Cho:
Combinatorial Bayesian optimization with random mapping functions to convex polytopes. UAI 2022: 1001-1011 - [i14]Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham W. Taylor:
On Evaluation Metrics for Graph Generative Models. CoRR abs/2201.09871 (2022) - [i13]Jungtaek Kim, Seungjin Choi:
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization. CoRR abs/2202.10669 (2022) - [i12]Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, Minsu Cho:
Learning to Assemble Geometric Shapes. CoRR abs/2205.11809 (2022) - [i11]Seokjun Ahn, Jungtaek Kim, Minsu Cho, Jaesik Park:
Sequential Brick Assembly with Efficient Constraint Satisfaction. CoRR abs/2210.01021 (2022) - 2021
- [j1]Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi:
Bayesian optimization with approximate set kernels. Mach. Learn. 110(5): 857-879 (2021) - [c7]Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho:
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. NeurIPS 2021: 5745-5757 - [i10]Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho:
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. CoRR abs/2110.15481 (2021) - 2020
- [c6]Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh:
Bootstrapping neural processes. NeurIPS 2020 - [c5]Jungtaek Kim, Seungjin Choi:
On Local Optimizers of Acquisition Functions in Bayesian Optimization. ECML/PKDD (2) 2020: 675-690 - [i9]Jungtaek Kim, Hyunsoo Chung, Minsu Cho, Jaesik Park:
Combinatorial 3D Shape Generation via Sequential Assembly. CoRR abs/2004.07414 (2020) - [i8]Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh:
Bootstrapping Neural Processes. CoRR abs/2008.02956 (2020) - [i7]Jungtaek Kim, Minsu Cho, Seungjin Choi:
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope. CoRR abs/2011.13094 (2020)
2010 – 2019
- 2019
- [c4]Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh:
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. ICML 2019: 3744-3753 - [i6]Jungtaek Kim, Seungjin Choi:
On Local Optimizers of Acquisition Functions in Bayesian Optimization. CoRR abs/1901.08350 (2019) - [i5]Minseop Park, Jungtaek Kim, Saehoon Kim, Yanbin Liu, Seungjin Choi:
MxML: Mixture of Meta-Learners for Few-Shot Classification. CoRR abs/1904.05658 (2019) - [i4]Jungtaek Kim, Seungjin Choi:
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization. CoRR abs/1905.07540 (2019) - [i3]Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi:
Bayesian Optimization over Sets. CoRR abs/1905.09780 (2019) - 2018
- [c3]Saehoon Kim, Jungtaek Kim, Seungjin Choi:
On the Optimal Bit Complexity of Circulant Binary Embedding. AAAI 2018: 3423-3430 - [c2]Jungtaek Kim, Seungjin Choi:
Clustering-Guided Gp-Ucb for Bayesian Optimization. ICASSP 2018: 2461-2465 - [c1]Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, Seungjin Choi:
Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks. ICASSP 2018: 2686-2690 - [i2]Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh:
Set Transformer. CoRR abs/1810.00825 (2018) - 2017
- [i1]Jungtaek Kim, Saehoon Kim, Seungjin Choi:
Learning to Transfer Initializations for Bayesian Hyperparameter Optimization. CoRR abs/1710.06219 (2017)
Coauthor Index
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