Brendan Tracey
Brendan Tracey
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Cited by
Magnetic control of tokamak plasmas through deep reinforcement learning
J Degrave, F Felici, J Buchli, M Neunert, B Tracey, F Carpanese, T Ewalds, ...
Nature 602 (7897), 414-419, 2022
On the information bottleneck theory of deep learning
AM Saxe, Y Bansal, J Dapello, M Advani, A Kolchinsky, BD Tracey, ...
Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124020, 2019
A machine learning strategy to assist turbulence model development
BD Tracey, K Duraisamy, JJ Alonso
53rd AIAA aerospace sciences meeting, 1287, 2015
Stanford university unstructured (SU2): Analysis and design technology for turbulent flows
F Palacios, TD Economon, A Aranake, SR Copeland, AK Lonkar, ...
52nd Aerospace Sciences Meeting, 0243, 2014
Nonlinear information bottleneck
A Kolchinsky, BD Tracey, DH Wolpert
Entropy 21 (12), 1181, 2019
Application of Supervised Learning to Quantify Uncertainties in Turbulence and Combustion Modeling
B Tracey, K Duraisamy, JJ Alonso
Estimating mixture entropy with pairwise distances
A Kolchinsky, BD Tracey
Entropy 19 (7), 361, 2017
From motor control to team play in simulated humanoid football
S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ...
Science Robotics 7 (69), eabo0235, 2022
Real world games look like spinning tops
WM Czarnecki, G Gidel, B Tracey, K Tuyls, S Omidshafiei, D Balduzzi, ...
Advances in Neural Information Processing Systems 33, 17443-17454, 2020
Cyber-physical security: A game theory model of humans interacting over control systems
S Backhaus, R Bent, J Bono, R Lee, B Tracey, D Wolpert, D Xie, Y Yildiz
IEEE Transactions on Smart Grid 4 (4), 2320-2327, 2013
Caveats for information bottleneck in deterministic scenarios
A Kolchinsky, BD Tracey, S Van Kuyk
arXiv preprint arXiv:1808.07593, 2018
Deep reinforcement learning for event-driven multi-agent decision processes
K Menda, YC Chen, J Grana, JW Bono, BD Tracey, MJ Kochenderfer, ...
IEEE Transactions on Intelligent Transportation Systems 20 (4), 1259-1268, 2018
Upgrading from gaussian processes to student’st processes
BD Tracey, D Wolpert
2018 AIAA Non-Deterministic Approaches Conference, 1659, 2018
Diego de las Casas
J Degrave, F Felici, J Buchli, M Neunert, B Tracey, F Carpanese, T Ewalds, ...
Magnetic control of tokamak plasmas through deep reinforcement learning, 2022
Scale and information-processing thresholds in Holocene social evolution
J Shin, MH Price, DH Wolpert, H Shimao, B Tracey, TA Kohler
Nature communications 11 (1), 2394, 2020
Using supervised learning to improve Monte Carlo integral estimation
B Tracey, D Wolpert, JJ Alonso
AIAA journal 51 (8), 2015-2023, 2013
Air vehicle design and technology considerations for an electric VTOL metro-regional public transportation system
J Sinsay, J Alonso, D Kontinos, J Melton, S Grabbe
12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference†…, 2012
Counter-factual reinforcement learning: How to model decision-makers that anticipate the future
R Lee, DH Wolpert, J Bono, S Backhaus, R Bent, B Tracey
Decision making and imperfection, 101-128, 2013
Probabilistic simulation of multi-stage decisions for operation of a fractionated satellite mission
M Daniels, J Irvine, B Tracey, W Schram, ME Pate-Cornell
2011 Aerospace Conference, 1-16, 2011
Reducing the error of Monte Carlo algorithms by learning control variates
BD Tracey, DH Wolpert
arXiv preprint arXiv:1606.02261, 2016
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