| Learning to embed time series patches independently S Lee, T Park, K Lee ICLR 2024, NeurIPSW 2023 Oral, 2023 | 71 | 2023 |
| Soft contrastive learning for time series S Lee, T Park, K Lee ICLR 2024 Spotlight, NeurIPSW 2023, 2023 | 65 | 2023 |
| Improving the Gibbs sampler T Park, S Lee WIREs Computational Statistics 14 (2), e1546, 2022 | 16 | 2022 |
| ANT: Adaptive Noise Schedule for Time Series Diffusion Models S Lee, K Lee, T Park NeurIPS 2024, 2024 | 8 | 2024 |
| Partial Channel Dependence with Channel Masks for Time Series Foundation Models S Lee, T Park, K Lee NeurIPSW 2024 Oral, 2024 | 4 | 2024 |
| Sequential Order-Robust Mamba for Time Series Forecasting S Lee, J Hong, K Lee, T Park NeurIPSW 2024, 2024 | 2 | 2024 |
| Channel Normalization for Time Series Channel Identification S Lee, T Park, K Lee ICML 2025, 2025 | | 2025 |
| Hierarchical multi-task learning with self-supervised auxiliary task S Lee, T Park The Korean Journal of Applied Statistics 37 (5), 631-641, 2024 | | 2024 |
Taeyoung ParkProfessor, Department of Applied Statistics, Yonsei UniversityVerified email at yonsei.ac.kr
