Brendan O'Donoghue
Brendan O'Donoghue
Stanford University, DeepMind
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Zitiert von
Zitiert von
Clinically applicable deep learning for diagnosis and referral in retinal disease
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
Nature medicine 24 (9), 1342-1350, 2018
Fast alternating direction optimization methods
T Goldstein, B O'Donoghue, S Setzer, R Baraniuk
SIAM Journal on Imaging Sciences 7 (3), 1588-1623, 2014
Adaptive restart for accelerated gradient schemes
B O’Donoghue, E Candes
Foundations of computational mathematics 15 (3), 715-732, 2015
Conic optimization via operator splitting and homogeneous self-dual embedding
B O’Donoghue, E Chu, N Parikh, S Boyd
Journal of Optimization Theory and Applications 169 (3), 1042-1068, 2016
Adversarial risk and the dangers of evaluating against weak attacks
J Uesato, B O'Donoghue, A Oord, P Kohli
35th International Conference on Machine Learning (ICML), 2018
Combining policy gradient and Q-learning
B O'Donoghue, R Munos, K Kavukcuoglu, V Mnih
International Conference on Learning Representations, 2017
A splitting method for optimal control
B O'Donoghue, G Stathopoulos, S Boyd
IEEE Transactions on Control Systems Technology 21 (6), 2432-2442, 2013
The Uncertainty Bellman Equation and Exploration
B O'Donoghue, I Osband, R Munos, V Mnih
International Conference on Machine Learning, 2018
Training verified learners with learned verifiers
K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ...
arXiv preprint arXiv:1805.10265, 2018
Solving mixed integer programs using neural networks
V Nair, S Bartunov, F Gimeno, I Von Glehn, P Lichocki, I Lobov, ...
arXiv preprint arXiv:2012.13349, 2020
SCS: Splitting conic solver
B O’Donoghue, E Chu, N Parikh, S Boyd
Globally convergent type-I Anderson acceleration for nonsmooth fixed-point iterations
J Zhang, B O'Donoghue, S Boyd
SIAM Journal on Optimization 30 (4), 3170-3197, 2020
Large-scale convex optimization for dense wireless cooperative networks
Y Shi, J Zhang, B O'Donoghue, KB Letaief
IEEE Transactions on Signal Processing 63 (18), 4729-4743, 2015
Approximate dynamic programming via iterated Bellman inequalities
Y Wang, B O'Donoghue, S Boyd
International Journal of Robust and Nonlinear Control 25 (10), 1472-1496, 2015
Performance bounds and suboptimal policies for multi–period investment
S Boyd, MT Mueller, B O’Donoghue, Y Wang
Foundations and Trends® in Optimization 1 (1), 1-72, 2013
Hamiltonian descent methods
CJ Maddison, D Paulin, YW Teh, B O'Donoghue, A Doucet
arXiv preprint arXiv:1809.05042, 2018
Sample efficient reinforcement learning with REINFORCE
J Zhang, J Kim, B O'Donoghue, S Boyd
Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10887 …, 2021
Verification of non-linear specifications for neural networks
C Qin, B O'Donoghue, R Bunel, R Stanforth, S Gowal, J Uesato, ...
arXiv preprint arXiv:1902.09592, 2019
Min-max approximate dynamic programming
B O'Donoghue, Y Wang, S Boyd
2011 IEEE International Symposium on Computer-Aided Control System Design …, 2011
Variational Bayesian reinforcement learning with regret bounds
B O'Donoghue
Advances in Neural Information Processing Systems 34, 28208-28221, 2021
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