Learning to Transfer Initializations for Bayesian Hyperparameter Optimization. - Jungtaek Kim 001; Saehoon Kim; Seungjin Choi (CoRR, 2017)

On the Optimal Bit Complexity of Circulant Binary Embedding. - Saehoon Kim; Jungtaek Kim 001; Seungjin Choi (AAAI, 2018)

Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks. - Inhyuk Jo; Jungtaek Kim 001; Hyohyeong Kang; Yong-Deok Kim; Seungjin Choi (ICASSP, 2018)

Clustering-Guided Gp-Ucb for Bayesian Optimization. - Jungtaek Kim 001; Seungjin Choi (ICASSP, 2018)

Set Transformer. - Juho Lee 001; Yoonho Lee 001; Jungtaek Kim 001; Adam R. Kosiorek; Seungjin Choi; Yee Whye Teh (CoRR, 2018)

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. - Juho Lee 001; Yoonho Lee 001; Jungtaek Kim 001; Adam R. Kosiorek; Seungjin Choi; Yee Whye Teh (ICML, 2019)

MxML: Mixture of Meta-Learners for Few-Shot Classification. - Minseop Park; Jungtaek Kim 001; Saehoon Kim; Yanbin Liu; Seungjin Choi (CoRR, 2019)

Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization. - Jungtaek Kim 001; Seungjin Choi (CoRR, 2019)

Bayesian Optimization over Sets. - Jungtaek Kim 001; Michael McCourt; Tackgeun You; Saehoon Kim; Seungjin Choi (CoRR, 2019)

Bootstrapping neural processes. - Juho Lee 001; Yoonho Lee 001; Jungtaek Kim 001; Eunho Yang; Sung Ju Hwang; Yee Whye Teh (NeurIPS, 2020)

On Local Optimizers of Acquisition Functions in Bayesian Optimization. - Jungtaek Kim 001; Seungjin Choi (ECML/PKDD (2), 2020)

Combinatorial 3D Shape Generation via Sequential Assembly. - Jungtaek Kim 001; Hyunsoo Chung; Minsu Cho; Jaesik Park (CoRR, 2020)

Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope. - Jungtaek Kim 001; Minsu Cho; Seungjin Choi (CoRR, 2020)

Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. - Hyunsoo Chung; Jungtaek Kim 001; Boris Knyazev 001; Jinhwi Lee; Graham W. Taylor; Jaesik Park; Minsu Cho (NeurIPS, 2021)

Bayesian optimization with approximate set kernels. - Jungtaek Kim 001; Michael McCourt; Tackgeun You; Saehoon Kim; Seungjin Choi (Mach. Learn., 2021)

On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization. - Jungtaek Kim 001; Seungjin Choi (AISTATS, 2022)

On Evaluation Metrics for Graph Generative Models. - Rylee Thompson; Boris Knyazev 001; Elahe Ghalebi; Jungtaek Kim 001; Graham W. Taylor (ICLR, 2022)

Learning to Assemble Geometric Shapes. - Jinhwi Lee; Jungtaek Kim 001; Hyunsoo Chung; Jaesik Park; Minsu Cho (IJCAI, 2022)

Combinatorial Bayesian optimization with random mapping functions to convex polytopes. - Jungtaek Kim 001; Seungjin Choi; Minsu Cho (UAI, 2022)

Sequential Brick Assembly with Efficient Constraint Satisfaction. - Seokjun Ahn; Jungtaek Kim 001; Minsu Cho; Jaesik Park (CoRR, 2022)

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations. - Jungtaek Kim 001; Mingxuan Li; Oliver Hinder; Paul W. Leu (NeurIPS, 2023)

Generative Neural Fields by Mixtures of Neural Implicit Functions. - Tackgeun You; Mijeong Kim 002; Jungtaek Kim 001; Bohyung Han (NeurIPS, 2023)

Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning. - Jungtaek Kim 001 (CoRR, 2023)

BayesO: A Bayesian optimization framework in Python. - Jungtaek Kim 001; Seungjin Choi (J. Open Source Softw., 2023)

BayesO: A Bayesian optimization framework in Python. - Jungtaek Kim 001; Seungjin Choi (Zenodo, 2023)

Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity. - Hyunsoo Chung; Jungtaek Kim 001; Hyungeun Jo; Hyungwon Choi (CIKM, 2024)

Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions. - Jungtaek Kim 001; Jeongbeen Yoon; Minsu Cho (ICLR, 2024)

Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. - Kwang-Sung Jun; Jungtaek Kim 001 (ICML, 2024)

Model Fusion through Bayesian Optimization in Language Model Fine-Tuning. - Chaeyun Jang; Hyungi Lee; Jungtaek Kim 001; Juho Lee 001 (NeurIPS, 2024)

Beyond Regrets: Geometric Metrics for Bayesian Optimization. - Jungtaek Kim 001 (CoRR, 2024)

Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction. - Seokjun Ahn; Jungtaek Kim 001; Minsu Cho; Jaesik Park (Trans. Mach. Learn. Res., 2024)

BayesO: A Bayesian optimization framework in Python. - Jungtaek Kim 001; Seungjin Choi (Zenodo, 2024)

VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data. - Thomas Zeng 003; Shuibai Zhang; Shutong Wu; Christian Classen; Daewon Chae; Ethan Ewer; Minjae Lee; Heeju Kim; Wonjun Kang; Jackson Kunde; Ying Fan; Jungtaek Kim 001; et al. (CoRR, 2025)