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Youngseog Chung
Youngseog Chung
Verified email at cs.cmu.edu
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Cited by
Year
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
782021
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
782021
Offline contextual bayesian optimization
I Char, Y Chung, W Neiswanger, K Kandasamy, AO Nelson, M Boyer, ...
Advances in Neural Information Processing Systems 32, 2019
402019
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
222021
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
202022
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
142023
How useful are gradients for ood detection really?
C Igoe, Y Chung, I Char, J Schneider
arXiv preprint arXiv:2205.10439, 2022
132022
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
112020
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
72020
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
62021
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
6
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
32021
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
12023
Parity Calibration
Y Chung, A Rumack, C Gupta
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial …, 2023
12023
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
12022
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
12000
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
2024
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
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
Post-nonlinear Causal Model with Deep Neural Networks
Y Chung, J Kim, T Yan, H Zhou
2019
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