Brendan O'Donoghue


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