Brendan O'Donoghue, Ph.D. |

Ph.D., M.S., Electrical Engineering, Stanford University, January 2013

B.A., M.A., M.Eng., Information and Computer Engineering, Gonville and Caius College, Cambridge University, June 2007

Convex optimization

Machine Learning

Reinforcement Learning

Dynamic systems and control

SCS: C package for solving convex cone problems via operator splitting

- Variational Bayesian optimistic sampling
B. O'Donoghue and T. Lattimore

- Evaluating predictive distributions: Does Bayesian deep learning work?
I. Osband, Z. Wen, S. Asghari, V. Dwaracherla, B. Hao, M. Ibrahimi, D. Lawson, X. Lu, B. O'Donoghue, and B. Van Roy

- Practical large-scale linear programming using primal-dual hybrid gradient
D. Applegate, M. Díaz, O. Hinder, H. Lu, M. Lubin, B. O'Donoghue, and W. Schudy

- Reward is enough for convex MDPs
T. Zahavy, B. O'Donoghue, G. Desjardins, and S. Singh

- Discovering diverse nearly optimal policies with successor features
T. Zahavy, B. O'Donoghue, A. Barreto, V. Mnih, S. Flennerhag, and S. Singh

- 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

- 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, S. Setzer, and R. Baraniuk

- 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

- Suboptimal Control Policies via Convex Optimization
B. O'Donoghue