| 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 | 2014 | 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 | 840 | 2014 |
| Adaptive restart for accelerated gradient schemes B O’Donoghue, E Candes Foundations of computational mathematics 15 (3), 715-732, 2015 | 774 | 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 | 660 | 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 | 541 | 2018 |
| Combining policy gradient and Q-learning B O'Donoghue, R Munos, K Kavukcuoglu, V Mnih International Conference on Learning Representations, 2017 | 221 | 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 | 196 | 2013 |
| The Uncertainty Bellman Equation and Exploration B O'Donoghue, I Osband, R Munos, V Mnih International Conference on Machine Learning, 2018 | 193 | 2018 |
| Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018 | 163 | 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 | 161 | 2020 |
| SCS: Splitting conic solver B O’Donoghue, E Chu, N Parikh, S Boyd | 125* | 2016 |
| 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 | 123 | 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 | 115 | 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 | 101* | 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 | 81 | 2013 |
| 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 | 56 | 2021 |
| Hamiltonian descent methods CJ Maddison, D Paulin, YW Teh, B O'Donoghue, A Doucet arXiv preprint arXiv:1809.05042, 2018 | 55 | 2018 |
| 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 | 38 | 2019 |
| Practical large-scale linear programming using primal-dual hybrid gradient D Applegate, M Díaz, O Hinder, H Lu, M Lubin, B O'Donoghue, W Schudy Advances in Neural Information Processing Systems 34, 20243-20257, 2021 | 35 | 2021 |
| Reward is enough for convex mdps T Zahavy, B O'Donoghue, G Desjardins, S Singh Advances in Neural Information Processing Systems 34, 25746-25759, 2021 | 35 | 2021 |
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerified email at stanford.edu
