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Ian Char
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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
952021
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
902021
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
462019
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
36*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
252022
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
212023
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
202014
How useful are gradients for ood detection really?
C Igoe, Y Chung, I Char, J Schneider
arXiv preprint arXiv:2205.10439, 2022
182022
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
122020
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
102022
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
72022
Bats: Best action trajectory stitching
I Char, V Mehta, A Villaflor, JM Dolan, J Schneider
arXiv preprint arXiv:2204.12026, 2022
72022
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
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
52024
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
42023
Deep attentive variational inference
I Apostolopoulou, I Char, E Rosenfeld, A Dubrawski
International Conference on Learning Representations, 2021
42021
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
42021
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
22024
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
22024
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
12024
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