I. Char

发表

BATS: Best Action Trajectory Stitching

A. Villaflor, J. Dolan, Viraj Mehta, 2022, 2204.12026.

Stochastic Analysis of Minimal Automata Growth for Generalized Strings

M. Lladser, I. Char, 2020, Methodology and Computing in Applied Probability.

Published at ICLR 2020 Integration of Deep Neural Models and Differential Equations Workshop NEURAL DYNAMICAL SYSTEMS

J. Schneider, W. Neiswanger, M. Boyer, 2020 .

D EEP A TTENTIVE V ARIATIONAL I NFERENCE

A. Dubrawski, Elan Rosenfeld, I. Char, 2022 .

DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy

C. Kamath, R. Churchill, D. Elder, 2021, Nuclear Fusion.

Exploration via Planning for Information about the Optimal Trajectory

J. Schneider, S. Ermon, W. Neiswanger, 2022, NeurIPS.

How Useful are Gradients for OOD Detection Really?

J. Schneider, Youngseog Chung, I. Char, 2022, ArXiv.

Toward a non-intrusive, physio- behavioral biometric for smartphones

Larry S. Davis, Rama Chellappa, Yan Chen, 2014, MobileHCI '14.

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction

J. Schneider, W. Neiswanger, M. Boyer, 2020, 2021 60th IEEE Conference on Decision and Control (CDC).

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification pdf

Willie Neiswanger, Jeff Schneider, Ian Char, 2020, NeurIPS.

Near-optimal Policy Identification in Active Reinforcement Learning

Xiang Li, Willie Neiswanger, J. Schneider, 2022, ICLR.

Offline Contextual Bayesian Optimization

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, 2019, NeurIPS.

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification pdf

Willie Neiswanger, Jeff Schneider, Ian Char, 2021, ArXiv.

Algorithmic Construction and Stochastic Analysis of Optimal Automata for Generalized Strings

Ian Guo-fan Char, I. Char, 2018 .

Offline Model-Based Reinforcement Learning for Tokamak Control

M. Boyer, Viraj Mehta, Youngseog Chung, 2023, L4DC.

Offline Contextual Bayesian Optimization for Nuclear Fusion pdf

Kirthevasan Kandasamy, Andrew, Willie Neiswanger, 2020, ArXiv.

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks

J. Schneider, I. Char, 2023, arXiv.org.

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks

J. Schneider, I. Char, 2023, NeurIPS.

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

Youngseog Chung, E. Kolemen, I. Char, 2024, ArXiv.