| One4all user representation for recommender systems in e-commerce K Shin, H Kwak, KM Kim, M Kim, YJ Park, J Jeong, S Jung arXiv preprint arXiv:2106.00573, 2021 | 41 | 2021 |
| Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems JS Ha, YJ Park, HJ Chae, SS Park, HL Choi Advances in Neural Information Processing Systems, 8927-8938, 2018 | 33 | 2018 |
| Tripartite heterogeneous graph propagation for large-scale social recommendation KM Kim, D Kwak, H Kwak, YJ Park, S Sim, JH Cho, M Kim, J Kwon, ... 13th ACM Conference on Recommender Systems, 2019 | 27* | 2019 |
| Deep Gaussian process-based Bayesian inference for contaminant source localization YJ Park, PM Tagade, HL Choi Ieee Access 6, 49432-49449, 2018 | 18 | 2018 |
| Vq-ar: Vector quantized autoregressive probabilistic time series forecasting K Rasul, YJ Park, MN Ramström, KM Kim arXiv preprint arXiv:2205.15894, 2022 | 13 | 2022 |
| Online Gaussian process state-space model: Learning and planning for partially observable dynamical systems SS Park, YJ Park, Y Min, HL Choi International Journal of Control, Automation and Systems 20 (2), 601-617, 2022 | 13 | 2022 |
| Quantifying representation reliability in self-supervised learning models YJ Park, H Wang, S Ardeshir, N Azizan arXiv preprint arXiv:2306.00206, 2023 | 10 | 2023 |
| A worrying analysis of probabilistic time-series models for sales forecasting S Jung, KM Kim, H Kwak, YJ Park PMLR, 2020 | 10 | 2020 |
| Uncertainty-aware meta-learning for multimodal task distributions C Almecija, A Sharma, YJ Park, N Azizan 6th Workshop on Meta-Learning at NeurIPS 2022, 2022 | 9 | 2022 |
| Multi-manifold learning for large-scale targeted advertising system K Shin, YJ Park, KM Kim, S Kwon arXiv preprint arXiv:2007.02334, 2020 | 8 | 2020 |
| Global-local item embedding for temporal set prediction S Jung, YJ Park, J Jeong, KM Kim, H Kim, M Kim, H Kwak Proceedings of the 15th ACM conference on recommender systems, 674-679, 2021 | 7 | 2021 |
| A neural process approach for probabilistic reconstruction of no-data gaps in lunar digital elevation maps YJ Park, HL Choi Aerospace Science and Technology 113, 106672, 2021 | 7 | 2021 |
| A large-scale ensemble learning framework for demand forecasting YJ Park, D Kim, F Odermatt, J Lee, KM Kim 2022 IEEE International Conference on Data Mining (ICDM), 378-387, 2022 | 6 | 2022 |
| div2vec: diversity-emphasized node embedding J Jeong, JM Yun, H Keam, YJ Park, Z Park, J Cho arXiv preprint arXiv:2009.09588, 2020 | 6 | 2020 |
| Know What You Don't Know: Uncertainty Calibration of Process Reward Models YJ Park, K Greenewald, K Alim, H Wang, N Azizan arXiv preprint arXiv:2506.09338, 2025 | 5 | 2025 |
| Distilling a hierarchical policy for planning and control via representation and reinforcement learning JS Ha, YJ Park, HJ Chae, SS Park, HL Choi 2021 IEEE International Conference on Robotics and Automation (ICRA), 4459-4466, 2021 | 4 | 2021 |
| Hop sampling: A simple regularized graph learning for non-stationary environments YJ Park, K Shin, KM Kim arXiv preprint arXiv:2006.14897, 2020 | 4 | 2020 |
| Efficient sensor network planning based on approximate potential games SJ Lee, YJ Park, HL Choi International Journal of Distributed Sensor Networks 14 (6), 1550147718781454, 2018 | 3 | 2018 |
| Quantifying the Reliability of Predictions in Detection Transformers: Object-Level Calibration and Image-Level Uncertainty YJ Park, C Sobolewski, N Azizan arXiv preprint arXiv:2412.01782, 2024 | 2 | 2024 |
| Bayesian nonparametric state-space model for system identification with distinguishable multimodal dynamics YJ Park, SS Park, HL Choi Journal of Aerospace Information Systems 18 (3), 116-131, 2021 | 2* | 2021 |
Han-Lim ChoiKorea Advanced Institute of Science and TechnologyVerified email at kaist.ac.kr
