| 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 | 98 | 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 | 95 | 2021 |
| Offline Contextual Bayesian Optimization I Char, Y Chung, W Neiswanger, K Kandasamy, AO Nelson, M Boyer, ... Advances in Neural Information Processing Systems, 4629-4640, 2019 | 49 | 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 | 37* | 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 | 26 | 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 | 23 | 2023 |
| Toward a non-intrusive, physio-behavioral biometric for smartphones E Vasiete, Y Chen, I Char, T Yeh, V Patel, L Davis, R Chellappa Proceedings of the 16th international conference on Human-computer …, 2014 | 20 | 2014 |
| 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 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 |
| Exploration via planning for information about the optimal trajectory V Mehta, I Char, J Abbate, R Conlin, M Boyer, S Ermon, J Schneider, ... Advances in Neural Information Processing Systems 35, 28761-28775, 2022 | 11 | 2022 |
| Near-optimal policy identification in active reinforcement learning X Li, V Mehta, J Kirschner, I Char, W Neiswanger, J Schneider, A Krause, ... arXiv preprint arXiv:2212.09510, 2022 | 7 | 2022 |
| Bats: Best action trajectory stitching I Char, V Mehta, A Villaflor, JM Dolan, J Schneider arXiv preprint arXiv:2204.12026, 2022 | 7 | 2022 |
| 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 |
| PID-inspired inductive biases for deep reinforcement learning in partially observable control tasks I Char, J Schneider Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Towards llms as operational copilots for fusion reactors V Mehta, J Abbate, A Wang, A Rothstein, I Char, J Schneider, E Kolemen, ... NeurIPS 2023 AI for Science Workshop, 2023 | 5 | 2023 |
| Deep attentive variational inference I Apostolopoulou, I Char, E Rosenfeld, A Dubrawski International Conference on Learning Representations, 2021 | 4 | 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 |
| 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 | 3 | 2024 |
| Automated experimental design of safe rampdowns via probabilistic machine learning V Mehta, J Barr, J Abbate, MD Boyer, I Char, W Neiswanger, E Kolemen, ... Nuclear Fusion 64 (4), 046014, 2024 | 2 | 2024 |
| DIII-D research to provide solutions for ITER and fusion energy CT Holcomb, J Abbate, A Abe, A Abrams, P Adebayo-Ige, S Agabian, ... Nuclear Fusion 64 (11), 112003, 2024 | 1 | 2024 |
Jeff SchneiderCarnegie Mellon University在 cs.cmu.edu 的電子郵件地址已通過驗證
