Brendan O'Donoghue
Publications
 Discovering a set of policies for
the worst case reward
T. Zahavy, A. Barreto, D. Mankowitz, S. Hou, B. O'Donoghue, I. Kemaev, and S. Singh
 Solving Mixed Integer Programs Using Neural Networks
V. Nair, S. Bartunov, F. Gimeno, I. von Glehn, P. Lichocki, I. Lobov, B. O'Donoghue, N. Sonnerat, C. Tjandraatmadja, P. Wang et al.
 Sample Efficient Reinforcement Learning with REINFORCE
J. Zhang, J. Kim, B. O'Donoghue, and S. Boyd
 Stochastic matrix games with bandit feedback
B. O'Donoghue, T. Lattimore, and I. Osband
 Operator splitting for a homogeneous embedding of the monotone linear complementarity problem
B. O'Donoghue
 Making sense of reinforcement learning and probabilistic inference
B. O'Donoghue, I Osband, and C. Ionescu
 Hamiltonian descent for composite objectives
B. O'Donoghue and C. J. Maddison
 Visualizations of decision regions in the presence of adversarial examples
G. Swirszcz, B. O'Donoghue, and P. Kohli
 Verification of nonlinear specifications for neural networks
C. Qin, K. (Dj) Dvijotham, B. O’Donoghue, R. Bunel, R. Stanforth, S. Gowal, J. Uesato, G. Swirszcz, and P. Kohli
 Strength in numbers: Tradingoff robustness and computation via adversariallytrained ensembles
E. Grefenstette, R. Stanforth, B. O'Donoghue, J. Uesato, G. Swirszcz, and P. Kohli
 Hamiltonian descent methods
C. J. Maddison, D. Paulin, Y. W. Teh, B. O'Donoghue, and A. Doucet
 Clinically applicable deep learning for diagnosis and referral in retinal disease
J. De Fauw et al.
 Globally convergent typeI Anderson acceleration for nonsmooth fixedpoint iterations
J. Zhang, B. O'Donoghue, and S. Boyd
 Variational Bayesian reinforcement learning with regret bounds
B. O'Donoghue
 Training verified learners with learned verifiers
K. (Dj) Dvijotham, S. Gowal, R. Stanforth, R. Arandjelovic, B. O'Donoghue, J. Uesato, and P. Kohli
 Adversarial risk and the dangers of evaluating against weak attacks
J. Uesato, B. O'Donoghue, A. van den Oord, and P. Kohli
 The uncertainty Bellman equation and exploration
B. O'Donoghue, I. Osband, R. Munos, and V. Mnih
 Combining policy gradient and Qlearning
B. O'Donoghue, R. Munos, K. Kavukcuoglu, and V. Mnih
 Largescale convex optimization for dense wireless cooperative networks
Y. Shi, J. Zhang, B. O’Donoghue, and K. Letaief
 Conic optimization via operator splitting and homogeneous selfdual embedding
B. O'Donoghue, E. Chu, N. Parikh, and S. Boyd
 A primaldual operator splitting method for conic optimization
E. Chu, B. O'Donoghue, N. Parikh, and S. Boyd
 Approximate dynamic programming via iterated Bellman Inequalities
Y. Wang, B. O'Donoghue, and S. Boyd
 Iterated approximate value functions
B. O'Donoghue, Y. Wang, and S. Boyd
 A splitting method for optimal control
B. O'Donoghue, G. Stathopoulos, and S. Boyd
 Fast alternating direction optimization methods
T. Goldstein, B. O'Donoghue, and S. Setzer
 Performance bounds and suboptimal policies for multiperiod investment
S. Boyd, M. Mueller, B. O'Donoghue, and Y. Wang
 Adaptive restart for accelerated gradient schemes
B. O'Donoghue and E. J. Candès
 A spreadreturn meanreverting model for credit spread dynamics
B. O'Donoghue, M. Peacock, J. Lee, and L. Capriotti
 Minmax approximate dynamic programming
B. O'Donoghue, Y. Wang, and S. Boyd
Ph.D. Thesis
 Suboptimal Control Policies via Convex Optimization
B. O'Donoghue
