| Incremental lossless graph summarization J Ko, Y Kook, K Shin International Conference on Knowledge Discovery & Data Mining (KDD'20), 317-327, 2020 | 48 | 2020 |
| Sampling with Riemannian Hamiltonian Monte Carlo in a constrained space Y Kook, YT Lee, R Shen, S Vempala Advances in Neural Information Processing Systems (NeurIPS'22) 35, 31684-31696, 2022 | 30 | 2022 |
| Evolution of real-world hypergraphs: Patterns and models without oracles Y Kook, J Ko, K Shin International Conference on Data Mining (ICDM'20), 272-281, 2020 | 29 | 2020 |
| Vertex sparsification for edge connectivity P Chalermsook, S Das, Y Kook, B Laekhanukit, YP Liu, R Peng, M Sellke, ... Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA'21), 1206 …, 2021 | 26* | 2021 |
| Condition-number-independent convergence rate of Riemannian Hamiltonian Monte Carlo with numerical integrators Y Kook, YT Lee, R Shen, S Vempala Conference on Learning Theory (COLT'23), 4504-4569, 2023 | 12 | 2023 |
| Growth patterns and models of real-world hypergraphs J Ko, Y Kook, K Shin Knowledge and Information Systems (KAIS) 64 (11), 2883-2920, 2022 | 10 | 2022 |
| Sampling from the mean-field stationary distribution Y Kook, MS Zhang, S Chewi, MA Erdogdu, MB Li Conference on Learning Theory (COLT'24), 3099-3136, 2024 | 4 | 2024 |
| Understanding Adam optimizer via online learning of updates: Adam is FTRL in disguise K Ahn, Z Zhang, Y Kook, Y Dai arXiv preprint arXiv:2402.01567, 2024 | 4 | 2024 |
| Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling Y Kook, SS Vempala Conference on Learning Theory (COLT'24), 3137-3240, 2024 | 3 | 2024 |
| R\'enyi-infinity constrained sampling with membership queries Y Kook, MS Zhang arXiv preprint arXiv:2407.12967, 2024 | | 2024 |
| In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies Y Kook, SS Vempala, MS Zhang arXiv preprint arXiv:2405.01425, 2024 | | 2024 |
