Publications

2024

1. Minimizing Annual Reflection Loss in Fixed-Tilt Photovoltaic Modules Using Graded Refractive Index (GRIN) Anti-Reflective Glass

   Karinna Martin, Katie Shanks, Yipeng Liu, Jungtaek Kim, Sajad Haghanifar, Mehdi Zarei, Sooraj Sharma, and Paul W. Leu

   Solar Energy, 2024

   Bib Website

   @article{MartinK2024se,
     title = {Minimizing Annual Reflection Loss in Fixed-Tilt Photovoltaic Modules Using Graded Refractive Index (GRIN) Anti-Reflective Glass},
     author = {Martin, Karinna and Shanks, Katie and Liu, Yipeng and Kim, Jungtaek and Haghanifar, Sajad and Zarei, Mehdi and Sharma, Sooraj and Leu, Paul W.},
     journal = {Solar Energy},
     volume = {272},
     pages = {112424},
     year = {2024}
   }

2. Flexible Embedded Metal Meshes by Sputter-Free Crack Lithography for Transparent Electrodes and Electromagnetic Interference Shielding

   Mehdi Zarei, Mingxuan Li, Elizabeth E. Medvedeva, Sooraj Sharma, Jungtaek Kim, Zefan Shao, S. Brett Walker, Melbs LeMieux, Qihan Liu, and Paul W. Leu

   ACS Applied Materials & Interfaces, 2024

   Bib Website

   @article{ZareiM2024acsami,
     title = {Flexible Embedded Metal Meshes by Sputter-Free Crack Lithography for Transparent Electrodes and Electromagnetic Interference Shielding},
     author = {Zarei, Mehdi and Li, Mingxuan and Medvedeva, Elizabeth E. and Sharma, Sooraj and Kim, Jungtaek and Shao, Zefan and Walker, S. Brett and LeMieux, Melbs and Liu, Qihan and Leu, Paul W.},
     journal = {ACS Applied Materials \& Interfaces},
     volume = {16},
     number = {5},
     pages = {6382--6393},
     year = {2024}
   }

3. ICLR

   Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions

   Jungtaek Kim, Jeongbeen Yoon, and Minsu Cho

   In Proceedings of the International Conference on Learning Representations (ICLR), 2024

   arXiv Bib

   @inproceedings{KimJ2024iclr,
     title = {Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions},
     author = {Kim, Jungtaek and Yoon, Jeongbeen and Cho, Minsu},
     booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
     year = {2024},
     address = {Vienna, Austria}
   }

4. Multi-BOWS: Multi-Fidelity Multi-Objective Bayesian Optimization with Warm Starts for Nanophotonic Structure Design

   Jungtaek Kim, Mingxuan Li, Yirong Li, Andrés Gómez, Oliver Hinder, and Paul W. Leu

   Digital Discovery, 2024

   Bib Website

   @article{KimJ2024dd,
     title = {{Multi-BOWS}: Multi-Fidelity Multi-Objective {Bayesian} Optimization with Warm Starts for Nanophotonic Structure Design},
     author = {Kim, Jungtaek and Li, Mingxuan and Li, Yirong and G\'{o}mez, Andr\'{e}s and Hinder, Oliver and Leu, Paul W.},
     journal = {Digital Discovery},
     volume = {3},
     number = {2},
     pages = {381--391},
     year = {2024}
   }

2023

1. NeurIPS-W

   Model Fusion through Bayesian Optimization in Language Model Fine-Tuning

   Chaeyun Jang, Jungtaek Kim, Hyungi Lee, and Juho Lee

   In Neural Information Processing Systems Workshop on Efficient Natural Language and Speech Processing (ENLSP), 2023

   Bib PDF

   @inproceedings{JangC2023enlsp,
     title = {Model Fusion through {Bayesian} Optimization in Language Model Fine-Tuning},
     author = {Jang, Chaeyun and Kim, Jungtaek and Lee, Hyungi and Lee, Juho},
     booktitle = {Neural Information Processing Systems Workshop on Efficient Natural Language and Speech Processing (ENLSP)},
     year = {2023},
     address = {New Orleans, Louisiana, USA}
   }

2. NeurIPS-W

   Leveraging Uniformity of Normalized Embeddings for Sequential Recommendation

   Hyunsoo Chung, and Jungtaek Kim

   In Neural Information Processing Systems Workshop on Self-Supervised Learning - Theory and Practice (SSL-TP), 2023

   Bib PDF

   @inproceedings{ChungH2023ssltp,
     title = {Leveraging Uniformity of Normalized Embeddings for Sequential Recommendation},
     author = {Chung, Hyunsoo and Kim, Jungtaek},
     booktitle = {Neural Information Processing Systems Workshop on Self-Supervised Learning - Theory and Practice (SSL-TP)},
     year = {2023},
     address = {New Orleans, Louisiana, USA}
   }

3. JOSS

   BayesO: A Bayesian Optimization Framework in Python

   Jungtaek Kim, and Seungjin Choi

   Journal of Open Source Software, 2023

   Bib PDF

   @article{KimJ2023joss,
     title = {{BayesO}: A {Bayesian} Optimization Framework in {Python}},
     author = {Kim, Jungtaek and Choi, Seungjin},
     journal = {Journal of Open Source Software},
     volume = {8},
     number = {90},
     pages = {5320},
     year = {2023}
   }

4. NeurIPS

   Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations

   Jungtaek Kim, Mingxuan Li, Oliver Hinder, and Paul W. Leu

   In Advances in Neural Information Processing Systems (NeurIPS), 2023

   Datasets and Benchmarks Track

   arXiv Bib

   @inproceedings{KimJ2023neurips,
     title = {Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations},
     author = {Kim, Jungtaek and Li, Mingxuan and Hinder, Oliver and Leu, Paul W.},
     booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
     volume = {36},
     pages = {4685--4715},
     year = {2023},
     address = {New Orleans, Louisiana, USA},
     note = {Datasets and Benchmarks Track}
   }

5. NeurIPS

   Generative Neural Fields by Mixtures of Neural Implicit Functions

   Tackgeun You, Mijeong Kim, Jungtaek Kim, and Bohyung Han

   In Advances in Neural Information Processing Systems (NeurIPS), 2023

   arXiv Bib

   @inproceedings{YouT2023neurips,
     title = {Generative Neural Fields by Mixtures of Neural Implicit Functions},
     author = {You, Tackgeun and Kim, Mijeong and Kim, Jungtaek and Han, Bohyung},
     booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
     volume = {36},
     pages = {20352--20370},
     year = {2023},
     address = {New Orleans, Louisiana, USA}
   }

2022

1. UAI

   Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes

   Jungtaek Kim, Seungjin Choi¶, and Minsu Cho¶

   In Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2022

   arXiv Bib

   @inproceedings{KimJ2022uai,
     title = {Combinatorial {Bayesian} Optimization with Random Mapping Functions to Convex Polytopes},
     author = {Kim, Jungtaek and Choi¶, Seungjin and Cho¶, Minsu},
     booktitle = {Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence (UAI)},
     pages = {1001--1011},
     year = {2022},
     address = {Eindhoven, the Netherlands}
   }

2. IJCAI

   Learning to Assemble Geometric Shapes

   Jinhwi Lee*, Jungtaek Kim*, Hyunsoo Chung, Jaesik Park, and Minsu Cho

   In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2022

   arXiv Bib

   @inproceedings{LeeJ2022ijcai,
     title = {Learning to Assemble Geometric Shapes},
     author = {Lee*, Jinhwi and Kim*, Jungtaek and Chung, Hyunsoo and Park, Jaesik and Cho, Minsu},
     booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)},
     pages = {1046--1052},
     year = {2022},
     address = {Vienna, Austria}
   }

3. ICLR

   On Evaluation Metrics for Graph Generative Models

   Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, and Graham W. Taylor

   In Proceedings of the International Conference on Learning Representations (ICLR), 2022

   arXiv Bib

   @inproceedings{ThompsonR2022iclr,
     title = {On Evaluation Metrics for Graph Generative Models},
     author = {Thompson, Rylee and Knyazev, Boris and Ghalebi, Elahe and Kim, Jungtaek and Taylor, Graham W.},
     booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
     year = {2022},
     address = {Virtual}
   }

4. AISTATS

   On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization

   Jungtaek Kim, and Seungjin Choi

   In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

   arXiv Bib

   @inproceedings{KimJ2022aistats,
     title = {On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization},
     author = {Kim, Jungtaek and Choi, Seungjin},
     booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
     pages = {4359--4375},
     year = {2022},
     address = {Virtual}
   }

2021

1. NeurIPS

   Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning

   Hyunsoo Chung*, Jungtaek Kim*, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, and Minsu Cho

   In Advances in Neural Information Processing Systems (NeurIPS), 2021

   arXiv Bib

   @inproceedings{ChungH2021neurips,
     title = {{Brick-by-Brick}: Combinatorial Construction with Deep Reinforcement Learning},
     author = {Chung*, Hyunsoo and Kim*, Jungtaek and Knyazev, Boris and Lee, Jinhwi and Taylor, Graham W. and Park, Jaesik and Cho, Minsu},
     booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
     volume = {34},
     pages = {5745--5757},
     year = {2021},
     address = {Virtual}
   }

2. MLJ

   Bayesian Optimization with Approximate Set Kernels

   Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi

   Machine Learning, 2021

   arXiv Bib Website

   @article{KimJ2021ml,
     title = {{Bayesian} Optimization with Approximate Set Kernels},
     author = {Kim, Jungtaek and McCourt, Michael and You, Tackgeun and Kim, Saehoon and Choi, Seungjin},
     journal = {Machine Learning},
     volume = {110},
     number = {5},
     pages = {857--879},
     year = {2021}
   }

2020

1. NeurIPS-W

   Combinatorial 3D Shape Generation via Sequential Assembly

   Jungtaek Kim, Hyunsoo Chung, Jinhwi Lee, Minsu Cho, and Jaesik Park

   In Neural Information Processing Systems Workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng), 2020

   arXiv Bib

   @inproceedings{KimJ2020ml4eng,
     author = {Kim, Jungtaek and Chung, Hyunsoo and Lee, Jinhwi and Cho, Minsu and Park, Jaesik},
     title = {Combinatorial {3D} Shape Generation via Sequential Assembly},
     booktitle = {Neural Information Processing Systems Workshop on Machine Learning for Engineering Modeling, Simulation, and Design (ML4Eng)},
     year = {2020},
     address = {Virtual}
   }

2. NeurIPS-W

   Fragment Relation Networks for Geometric Shape Assembly

   Jinhwi Lee*, Jungtaek Kim*, Hyunsoo Chung, Jaesik Park, and Minsu Cho

   In Neural Information Processing Systems Workshop on Learning Meets Combinatorial Algorithms (LMCA), 2020

   Bib PDF

   @inproceedings{LeeJ2020lmca,
     title = {Fragment Relation Networks for Geometric Shape Assembly},
     author = {Lee*, Jinhwi and Kim*, Jungtaek and Chung, Hyunsoo and Park, Jaesik and Cho, Minsu},
     booktitle = {Neural Information Processing Systems Workshop on Learning Meets Combinatorial Algorithms (LMCA)},
     year = {2020},
     address = {Virtual}
   }

3. NeurIPS

   Bootstrapping Neural Processes

   Juho Lee*, Yoonho Lee*, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, and Yee Whye Teh

   In Advances in Neural Information Processing Systems (NeurIPS), 2020

   arXiv Bib

   @inproceedings{LeeJ2020neurips,
     title = {Bootstrapping Neural Processes},
     author = {Lee*, Juho and Lee*, Yoonho and Kim, Jungtaek and Yang, Eunho and Hwang, Sung Ju and Teh, Yee Whye},
     booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
     volume = {33},
     pages = {6606--6615},
     year = {2020},
     address = {Virtual}
   }

4. ECML-PKDD

   On Local Optimizers of Acquisition Functions in Bayesian Optimization

   Jungtaek Kim, and Seungjin Choi

   In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020

   arXiv Bib

   @inproceedings{KimJ2020ecmlpkdd,
     title = {On Local Optimizers of Acquisition Functions in {Bayesian} Optimization},
     author = {Kim, Jungtaek and Choi, Seungjin},
     booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
     pages = {675--690},
     year = {2020},
     address = {Virtual}
   }

2019

1. ICML-W

   Bayesian Optimization over Sets

   Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi

   In International Conference on Machine Learning Workshop on Automated Machine Learning (AutoML), 2019

   Bib PDF

   @inproceedings{KimJ2019automl,
     title = {{Bayesian} Optimization over Sets},
     author = {Kim, Jungtaek and McCourt, Michael and You, Tackgeun and Kim, Saehoon and Choi, Seungjin},
     booktitle = {International Conference on Machine Learning Workshop on Automated Machine Learning (AutoML)},
     year = {2019},
     address = {Long Beach, California, USA}
   }

2. ICML

   Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

   Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, and Yee Whye Teh

   In Proceedings of the International Conference on Machine Learning (ICML), 2019

   arXiv Bib

   @inproceedings{LeeJ2019icml,
     title = {{Set Transformer}: A Framework for Attention-based Permutation-Invariant Neural Networks},
     author = {Lee, Juho and Lee, Yoonho and Kim, Jungtaek and Kosiorek, Adam R. and Choi, Seungjin and Teh, Yee Whye},
     booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
     pages = {3744--3753},
     year = {2019},
     address = {Long Beach, California, USA}
   }

3. Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization

   Jungtaek Kim, and Seungjin Choi

   arXiv preprint arXiv:1905.07540, 2019

   arXiv Bib

   @article{KimJ2019preprint,
     title = {Practical {Bayesian} Optimization with Threshold-Guided Marginal Likelihood Maximization},
     author = {Kim, Jungtaek and Choi, Seungjin},
     journal = {{arXiv} preprint {arXiv}:1905.07540},
     year = {2019}
   }

4. MxML: Mixture of Meta-Learners for Few-Shot Classification

   Minseop Park, Jungtaek Kim, Saehoon Kim, Yanbin Liu, and Seungjin Choi

   arXiv preprint arXiv:1904.05658, 2019

   arXiv Bib

   @article{ParkM2019preprint,
     title = {{MxML}: Mixture of Meta-Learners for Few-Shot Classification},
     author = {Park, Minseop and Kim, Jungtaek and Kim, Saehoon and Liu, Yanbin and Choi, Seungjin},
     journal = {{arXiv} preprint {arXiv}:1904.05658},
     year = {2019}
   }

2018

1. NeurIPS-W

   TAEML: Task-Adaptive Ensemble of Meta-Learners

   Minseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, and Seungjin Choi

   In Neural Information Processing Systems Workshop on Meta-Learning (MetaLearn), 2018

   Bib PDF

   @inproceedings{ParkM2018metalearn,
     title = {{TAEML}: Task-Adaptive Ensemble of Meta-Learners},
     author = {Park, Minseop and Kim, Saehoon and Kim, Jungtaek and Liu, Yanbin and Choi, Seungjin},
     booktitle = {Neural Information Processing Systems Workshop on Meta-Learning (MetaLearn)},
     year = {2018},
     address = {Montreal, Quebec, Canada}
   }

2. ICML-W

   Automated Machine Learning for Soft Voting in an Ensemble of Tree-based Classifiers

   Jungtaek Kim, and Seungjin Choi

   In International Conference on Machine Learning Workshop on Automatic Machine Learning (AutoML), 2018

   Bib PDF

   @inproceedings{KimJ2018automl,
     title = {Automated Machine Learning for Soft Voting in an Ensemble of Tree-based Classifiers},
     author = {Kim, Jungtaek and Choi, Seungjin},
     booktitle = {International Conference on Machine Learning Workshop on Automatic Machine Learning (AutoML)},
     year = {2018},
     address = {Stockholm, Sweden}
   }

3. ICASSP

   Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks

   Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, and Seungjin Choi

   In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018

   Bib PDF

   @inproceedings{JoI2018icassp,
     title = {Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks},
     author = {Jo, Inhyuk and Kim, Jungtaek and Kang, Hyohyeong and Kim, Yong-Deok and Choi, Seungjin},
     booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     pages = {2686--2690},
     year = {2018},
     address = {Calgary, Alberta, Canada}
   }

4. ICASSP

   Clustering-Guided GP-UCB for Bayesian Optimization

   Jungtaek Kim, and Seungjin Choi

   In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018

   Bib PDF

   @inproceedings{KimJ2018icassp,
     title = {Clustering-Guided {GP-UCB} for {Bayesian} Optimization},
     author = {Kim, Jungtaek and Choi, Seungjin},
     booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     pages = {2461--2465},
     year = {2018},
     address = {Calgary, Alberta, Canada}
   }

5. AAAI

   On the Optimal Bit Complexity of Circulant Binary Embedding

   Saehoon Kim, Jungtaek Kim, and Seungjin Choi

   In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2018

   Bib PDF

   @inproceedings{KimS2018aaai,
     title = {On the Optimal Bit Complexity of Circulant Binary Embedding},
     author = {Kim, Saehoon and Kim, Jungtaek and Choi, Seungjin},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
     pages = {3423--3430},
     year = {2018},
     address = {New Orleans, Louisiana, USA}
   }

2017

1. NeurIPS-W

   Learning to Transfer Initializations for Bayesian Hyperparameter Optimization

   Jungtaek Kim, Saehoon Kim, and Seungjin Choi

   In Neural Information Processing Systems Workshop on Bayesian Optimization (BayesOpt), 2017

   Bib PDF

   @inproceedings{KimJ2017bayesopt,
     title = {Learning to Transfer Initializations for {Bayesian} Hyperparameter Optimization},
     author = {Kim, Jungtaek and Kim, Saehoon and Choi, Seungjin},
     booktitle = {Neural Information Processing Systems Workshop on Bayesian Optimization (BayesOpt)},
     year = {2017},
     address = {Long Beach, California, USA}
   }

2. Learning to Warm-Start Bayesian Hyperparameter Optimization

   Jungtaek Kim, Saehoon Kim, and Seungjin Choi

   arXiv preprint arXiv:1710.06219, 2017

   arXiv Bib

   @article{KimJ2017preprint,
     title = {Learning to Warm-Start {Bayesian} Hyperparameter Optimization},
     author = {Kim, Jungtaek and Kim, Saehoon and Choi, Seungjin},
     journal = {{arXiv} preprint {arXiv}:1710.06219},
     year = {2017}
   }

2016

1. ICML-W

   AutoML Challenge: AutoML Framework Using Random Space Partitioning Optimizer

   Jungtaek Kim, Jongheon Jeong, and Seungjin Choi

   In International Conference on Machine Learning Workshop on Automatic Machine Learning (AutoML), 2016

   Bib PDF

   @inproceedings{KimJ2016automl,
     title = {{AutoML} {Challenge}: {AutoML} Framework Using Random Space Partitioning Optimizer},
     author = {Kim, Jungtaek and Jeong, Jongheon and Choi, Seungjin},
     booktitle = {International Conference on Machine Learning Workshop on Automatic Machine Learning (AutoML)},
     year = {2016},
     address = {New York, New York, USA}
   }