| Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks J Lee, Y Lee, J Kim, A Kosiorek, S Choi, YW Teh Proceedings of the 36th International Conference on Machine Learning, 3744-3753, 2019 | 820 | 2019 |
| Learning to Warm-Start Bayesian Hyperparameter Optimization J Kim, S Kim, S Choi arXiv preprint arXiv:1710.06219, 2017 | 36* | 2017 |
| Open Set Recognition by Regularizing Classifier with Fake Data Generated by Generative Adversarial Networks I Jo, J Kim, H Kang, YD Kim, S Choi Proceedings of the 43rd IEEE International Conference on Acoustics, Speech …, 2018 | 35 | 2018 |
| Bootstrapping Neural Processes J Lee*, Y Lee*, J Kim, E Yang, SJ Hwang, YW Teh Advances in Neural Information Processing Systems 33, 6606-6615, 2020 | 19 | 2020 |
| On Evaluation Metrics for Graph Generative Models R Thompson, B Knyazev, E Ghalebi, J Kim, GW Taylor Proceedings of the 10th International Conference on Learning Representations, 2022 | 16 | 2022 |
| On Local Optimizers of Acquisition Functions in Bayesian Optimization J Kim, S Choi Proceedings of the European Conference on Machine Learning and Principles …, 2020 | 15 | 2020 |
| BayesO: A Bayesian Optimization Framework in Python J Kim, S Choi https://bayeso.org, 2017 | 11 | 2017 |
| Combinatorial 3D Shape Generation via Sequential Assembly J Kim, H Chung, M Cho, J Park NeurIPS Workshop on Machine Learning for Engineering Modeling, Simulation …, 2020 | 9 | 2020 |
| Bayesian Optimization over Sets J Kim, M McCourt, T You, S Kim, S Choi ICML Workshop on Automated Machine Learning, 2019 | 9* | 2019 |
| Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning H Chung*, J Kim*, B Knyazev, J Lee, GW Taylor, J Park, M Cho Advances in Neural Information Processing Systems 34, 5745-5757, 2021 | 8 | 2021 |
| Bayesian Optimization with Approximate Set Kernels J Kim, M McCourt, T You, S Kim, S Choi Machine Learning 110 (5), 857-879, 2021 | 8 | 2021 |
| MxML: Mixture of Meta-Learners for Few-Shot Classification M Park, J Kim, S Kim, Y Liu, S Choi arXiv preprint arXiv:1904.05658, 2019 | 8 | 2019 |
| AutoML Challenge: AutoML Framework Using Random Space Partitioning Optimizer J Kim, J Jeong, S Choi ICML Workshop on Automatic Machine Learning, 2016 | 6 | 2016 |
| Automated Machine Learning for Soft Voting in an Ensemble of Tree-based Classifiers J Kim, S Choi ICML Workshop on Automatic Machine Learning, 2018 | 4 | 2018 |
| Learning to Transfer Initializations for Bayesian Hyperparameter Optimization J Kim, S Kim, S Choi NeurIPS Workshop on Bayesian Optimization, 2017 | 4 | 2017 |
| Clustering-Guided GP-UCB for Bayesian Optimization J Kim, S Choi Proceedings of the 43rd IEEE International Conference on Acoustics, Speech …, 2018 | 3 | 2018 |
| On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization J Kim, S Choi Proceedings of the 25th International Conference on Artificial Intelligence …, 2022 | 2 | 2022 |
| TAEML: Task-Adaptive Ensemble of Meta-Learners M Park, S Kim, J Kim, Y Liu, S Choi NeurIPS Workshop on Meta-Learning, 2018 | 2 | 2018 |
| On the Optimal Bit Complexity of Circulant Binary Embedding S Kim, J Kim, S Choi Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 3423-3430, 2018 | 2 | 2018 |
| Sequential Brick Assembly with Efficient Constraint Satisfaction S Ahn, J Kim, M Cho, J Park arXiv preprint arXiv:2210.01021, 2022 | 1 | 2022 |
Seungjin ChoiDirector of Machine Learning Lab, IntellicodeVerified email at postech.ac.kr
