| 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 | 62 | 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 | 60 | 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 | 31 | 2019 |
| 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 |
| 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 | 17 | 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 | 15 | 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 |
| How Useful are Gradients for OOD Detection Really? C Igoe, Y Chung, I Char, J Schneider arXiv preprint arXiv:2205.10439, 2022 | 9 | 2022 |
| 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 | 6 | 2020 |
| Bats: Best action trajectory stitching I Char, V Mehta, A Villaflor, JM Dolan, J Schneider arXiv preprint arXiv:2204.12026, 2022 | 5 | 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 | 4 | 2023 |
| 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 | 3 | 2022 |
| 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 | 2 | 2022 |
| Deep Attentive Variational Inference I Apostolopoulou, I Char, E Rosenfeld, A Dubrawski International Conference on Learning Representations, 2021 | 2 | 2021 |
| 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 | 2 | 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 |
| Sample-efficient plasma control by planning for optimal trajectory information V Mehta, I Char, J Schneider, W Neiswanger, S Ermon, J Abbate, ... ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the …, 2022 | 1 | 2022 |
| Stochastic Analysis of Minimal Automata Growth for Generalized Strings IG Char, ME Lladser Methodology and Computing in Applied Probability, 2019 | 1* | 2019 |
| Disruption Prediction via Deep Recurrent Neural Networks R Saxena, Y Chung, I Char, J Abbate, J Schneider Bulletin of the American Physical Society, 2023 | | 2023 |
| Automated Experimental Design of Safe Rampdowns via Probabilistic Machine Learning V Mehta, J Barr, J Abbate, I Char, W Neiswanger, M Boyer, E Kolemen, ... Bulletin of the American Physical Society, 2023 | | 2023 |
Jeff SchneiderCarnegie Mellon UniversityVerified email at cs.cmu.edu
