| Beyond pinball loss: Quantile methods for calibrated uncertainty quantification Y Chung, W Neiswanger, I Char, J Schneider Advances in Neural Information Processing Systems 34, 10971-10984, 2021 | 88 | 2021 |
| Uncertainty toolbox: an open-source library for assessing, visualizing, and improving uncertainty quantification Y Chung, I Char, H Guo, J Schneider, W Neiswanger arXiv preprint arXiv:2109.10254, 2021 | 82 | 2021 |
| Offline contextual bayesian optimization I Char, Y Chung, W Neiswanger, K Kandasamy, AO Nelson, M Boyer, ... Advances in Neural Information Processing Systems 32, 2019 | 42 | 2019 |
| Neural dynamical systems: Balancing structure and flexibility in physical prediction V Mehta, I Char, W Neiswanger, Y Chung, A Nelson, M Boyer, E Kolemen, ... 2021 60th IEEE Conference on Decision and Control (CDC), 3735-3742, 2021 | 27 | 2021 |
| DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy ME Fenstermacher, J Abbate, S Abe, T Abrams, M Adams, B Adamson, ... Nuclear Fusion 62 (4), 042024, 2022 | 22 | 2022 |
| How useful are gradients for ood detection really? C Igoe, Y Chung, I Char, J Schneider arXiv preprint arXiv:2205.10439, 2022 | 18 | 2022 |
| Offline model-based reinforcement learning for tokamak control I Char, J Abbate, L Bardóczi, M Boyer, Y Chung, R Conlin, K Erickson, ... Learning for Dynamics and Control Conference, 1357-1372, 2023 | 15 | 2023 |
| Offline contextual bayesian optimization for nuclear fusion Y Chung, I Char, W Neiswanger, K Kandasamy, AO Nelson, MD Boyer, ... arXiv preprint arXiv:2001.01793, 2020 | 12 | 2020 |
| Neural dynamical systems V Mehta, I Char, W Neiswanger, Y Chung, AO Nelson, MD Boyer, ... ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 8 | 2020 |
| A model-based reinforcement learning approach for beta control I Char, Y Chung, M Boyer, E Kolemen, J Schneider APS Division of Plasma Physics Meeting Abstracts 2021, PP11. 150, 2021 | 6 | 2021 |
| Machine learning for tokamak scenario optimization: combining accelerating physics models and empirical models M Boyer, J Wai, M Clement, E Kolemen, I Char, Y Chung, W Neiswanger, ... APS Division of Plasma Physics Meeting Abstracts 2021, PP11. 164, 2021 | 4 | 2021 |
| Uncertainty toolbox: An open-source library for assessing, visualizing, and improving uncertainty quantification. arXiv 2021 Y Chung, I Char, H Guo, J Schneider, W Neiswanger arXiv preprint arXiv:2109.10254, 0 | 4 | |
| What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions SK Choe, H Ahn, J Bae, K Zhao, M Kang, Y Chung, A Pratapa, ... arXiv preprint arXiv:2405.13954, 2024 | 2 | 2024 |
| Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning S Kataoka, Y Chung, SKS Ghasemipour, P Sanketi, SS Gu, I Mordatch arXiv preprint arXiv:2303.14870, 2023 | 2 | 2023 |
| Parity Calibration Y Chung, A Rumack, C Gupta Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial …, 2023 | 2 | 2023 |
| Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks I Char, Y Chung, J Abbate, E Kolemen, J Schneider arXiv preprint arXiv:2404.12416, 2024 | 1 | 2024 |
| Differential Rotation Control for the DIII-D Tokamak via Model-Based Reinforcement Learning I Char, J Abbate, V Mehta, Y Chung, R Conlin, K Erickson, M Boyer, ... APS Division of Plasma Physics Meeting Abstracts 2022, UP11. 102, 2022 | 1 | 2022 |
| Offline Model-Based Reinforcement Learning for Tokamak Control I Char, J Abbate, L Bardóczi, MD Boyer, Y Chung, R Conlin, K Erickson, ... Power (MW) 1500, 2500, 2000 | 1 | 2000 |
| Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making I Char, Y Chung, R Shah, W Neiswanger, J Schneider NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in …, 0 | 1 | |
| Sampling-based Multi-dimensional Recalibration Y Chung, I Char, J Schneider Forty-first International Conference on Machine Learning, 2024 | | 2024 |
