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 | 71 | 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 | 69 | 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 | 37 | 2019 |

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 | 18 | 2022 |

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 | 18 | 2021 |

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 | 12 | 2023 |

How Useful are Gradients for OOD Detection Really? C Igoe, Y Chung, I Char, J Schneider arXiv preprint arXiv:2205.10439, 2022 | 11 | 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 | 10 | 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 | 7 | 2020 |

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 | 5 | |

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 | 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 | 2 | 2021 |

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, ... Bulletin of the American Physical Society, 2022 | 1 | 2022 |

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 …, 2023 | | 2023 |

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 | | 2023 |

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks I Char, Y Chung, J Abbate, E Kolemen, J Schneider APS Division of Plasma Physics Meeting Abstracts 2023, UP11. 096, 2023 | | 2023 |

Disruption Prediction via Deep Recurrent Neural Networks R Saxena, Y Chung, I Char, J Abbate, J Schneider APS Division of Plasma Physics Meeting Abstracts 2023, UP11. 103, 2023 | | 2023 |

Parity Calibration Y Chung, A Rumack, C Gupta Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial …, 2023 | | 2023 |

Post-nonlinear Causal Model with Deep Neural Networks Y Chung, J Kim, T Yan, H Zhou | | 2019 |

Parity Calibration (Supplementary Material) Y Chung, A Rumack, C Gupta | | |