Toward a non-intrusive, physio- behavioral biometric for smartphones. - Esther Vasiete; Yan Chen 033; Ian Char; Tom Yeh; Vishal M. Patel; Larry S. Davis; Rama Chellappa (Mobile HCI, 2014)

Offline Contextual Bayesian Optimization. - Ian Char; Youngseog Chung; Willie Neiswanger; Kirthevasan Kandasamy; Oak Nelson; Mark D. Boyer; Egemen Kolemen (NeurIPS, 2019)

Offline Contextual Bayesian Optimization for Nuclear Fusion. - Youngseog Chung; Ian Char; Willie Neiswanger; Kirthevasan Kandasamy; Andrew Oakleigh Nelson; Mark D. Boyer; Egemen Kolemen; Jeff Schneider 001 (CoRR, 2020)

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. - Viraj Mehta; Ian Char; Willie Neiswanger; Youngseog Chung; Andrew Oakleigh Nelson; Mark D. Boyer; Egemen Kolemen; Jeff G. Schneider (CDC, 2021)

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. - Youngseog Chung; Willie Neiswanger; Ian Char; Jeff Schneider 001 (NeurIPS, 2021)

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. - Youngseog Chung; Ian Char; Han Guo; Jeff Schneider 001; Willie Neiswanger (CoRR, 2021)

Deep Attentive Variational Inference. - Ifigeneia Apostolopoulou; Ian Char; Elan Rosenfeld; Artur Dubrawski (ICLR, 2022)

Exploration via Planning for Information about the Optimal Trajectory. - Viraj Mehta; Ian Char; Joseph Abbate; Rory Conlin; Mark D. Boyer; Stefano Ermon; Jeff Schneider 001; Willie Neiswanger (NeurIPS, 2022)

BATS: Best Action Trajectory Stitching. - Ian Char; Viraj Mehta; Adam Villaflor; John M. Dolan; Jeff Schneider 001 (CoRR, 2022)

How Useful are Gradients for OOD Detection Really? - Conor Igoe; Youngseog Chung; Ian Char; Jeff Schneider 001 (CoRR, 2022)

Near-optimal Policy Identification in Active Reinforcement Learning. - Xiang Li; Viraj Mehta; Johannes Kirschner; Ian Char; Willie Neiswanger; Jeff Schneider 001; Andreas Krause 001; Ilija Bogunovic (ICLR, 2023)

Offline Model-Based Reinforcement Learning for Tokamak Control. - Ian Char; Joseph Abbate; Laszlo Bardoczi; Mark D. Boyer; Youngseog Chung; Rory Conlin; Keith Erickson; Viraj Mehta; Nathan Richner; Egemen Kolemen; Jeff G. Schneider (L4DC, 2023)

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks. - Ian Char; Jeff Schneider 001 (NeurIPS, 2023)

Sampling-based Multi-dimensional Recalibration. - Youngseog Chung; Ian Char; Jeff Schneider 001 (ICML, 2024)

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks. - Ian Char; Youngseog Chung; Joseph Abbate; Egemen Kolemen; Jeff G. Schneider (CoRR, 2024)

Advancing Model-Based Reinforcement Learning with Applications in Nuclear Fusion - Ian Char (2024)