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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 non-linear 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: Trading-off robustness and computation via adversarially-trained 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 type-I Anderson acceleration for non-smooth fixed-point 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 Q-learning
B. O'Donoghue, R. Munos, K. Kavukcuoglu, and V. Mnih - Large-scale convex optimization for dense wireless cooperative networks
Y. Shi, J. Zhang, B. O’Donoghue, and K. Letaief - Conic optimization via operator splitting and homogeneous self-dual embedding
B. O'Donoghue, E. Chu, N. Parikh, and S. Boyd - A primal-dual 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 multi-period 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 spread-return mean-reverting model for credit spread dynamics
B. O'Donoghue, M. Peacock, J. Lee, and L. Capriotti - Min-max approximate dynamic programming
B. O'Donoghue, Y. Wang, and S. Boyd
Ph.D. Thesis
- Suboptimal Control Policies via Convex Optimization
B. O'Donoghue
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