Brendan O'Donoghue, Ph.D. |

Ph.D., M.S., Computer Science,

Stanford University, January 2013B.A., M.A., M.Eng., Information and Computer Engineering,

Gonville and Caius College,

Cambridge University, June 2007

Artificial intelligence

Machine learning

Reinforcement learning

Dynamic systems and control

Convex optimization

SCS: Large-scale convex quadratic cone solver.

- POMRL: No-regret learning-to-plan with increasing horizons
K. Khetarpal, C. Vernade, B. O'Donoghue, S. Singh, and T. Zahavy

*Transactions on Machine Learning Research (TMLR)*, 2023.

- Operator splitting for a homogeneous embedding of the monotone linear complementarity problem
B. O'Donoghue

*SIAM Journal on Optimization*, 31(3), pp. 1999-2023, August 2021.

- Globally convergent type-I Anderson acceleration for non-smooth fixed-point iterations
J. Zhang, B. O'Donoghue, and S. Boyd

*SIAM Journal on Optimization*, 30(4), pp. 3170-3197, November 2020.

- Clinically applicable deep learning for diagnosis and referral in retinal disease
J. De Fauw,

*et al.*

*Nature Medicine*, 24(9), pp. 1342-1350, August 2018.

- Conic optimization via operator splitting and homogeneous self-dual embedding
B. O'Donoghue, E. Chu, N. Parikh, and S. Boyd

*Journal of Optimization Theory and Applications*, 169(3), pp. 1042-1068, June 2016.

- Large-scale convex optimization for dense wireless cooperative networks
Y. Shi, J. Zhang, B. O'Donoghue, and K. Letaief

*IEEE Transactions on Signal Processing*, 63(18), pp. 4729-4743, September 2015.

**IEEE 2016 SPS Young Author Best Paper Award**.

- Approximate dynamic programming via iterated Bellman Inequalities
Y. Wang, B. O'Donoghue, and S. Boyd

*International Journal of Robust and Nonlinear Control*, 25(10), pp. 1472-1496, July 2015.

- Adaptive restart for accelerated gradient schemes
B. O'Donoghue and E. J. Candès

*Foundations of computational mathematics*, 15(3), pp. 715-732, June 2015.

- Fast alternating direction optimization methods
T. Goldstein, B. O'Donoghue, S. Setzer, and R. Baraniuk

*SIAM Journal on Imaging Sciences*, 7(3), pp.1588-1623, August 2014.

- A spread-return mean-reverting model for credit spread dynamics
B. O'Donoghue, M. Peacock, J. Lee, and L. Capriotti

*International Journal of Theoretical and Applied Finance*, 17(3), pp. 1-14, May 2014.

- Performance bounds and suboptimal policies for multi-period investment
S. Boyd, M. Mueller, B. O'Donoghue, and Y. Wang

*Foundations and Trends in Optimization*, 1(1), pp. 1-69, January 2014.

- A splitting method for optimal control
B. O'Donoghue, G. Stathopoulos, and S. Boyd

*IEEE Transactions on Control Systems Technology*, 21(6), pp. 2432-2442, November 2013.

- Probabilistic inference in reinforcement learning done right
J. Tarbouriech, T. Lattimore, and B. O'Donoghue

*Advances in Neural Information Processing Systems (NeurIPS)*, 2023.

- Optimistic meta-gradients
S. Flennerhag, T. Zahavy, B. O'Donoghue, H. van Hasselt, A. György, and S. Singh

*Advances in Neural Information Processing Systems (NeurIPS)*, 2023.

- Efficient exploration via epistemic-risk-seeking policy optimization
B. O'Donoghue

*Proceedings of the International Conference on Machine Learning (ICML)*, 2023.

- ReLOAD: Reinforcement learning with optimistic ascent-descent for last-iterate convergence in constrained MDPs
T. Moskovitz, B. O'Donoghue, V. Veeriah, S. Flennerhag, S. Singh, and T. Zahavy

*Proceedings of the International Conference on Machine Learning (ICML)*, 2023.

- The neural testbed: Evaluating joint predictions
I. Osband, Z. Wen, S. Asghari, V. Dwaracherla, B. Hao, M. Ibrahimi, D. Lawson, X. Lu, B. O'Donoghue, and B. Van Roy

*Advances in Neural Information Processing Systems (NeurIPS)*, 2022.

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

**Spotlight***Advances in Neural Information Processing Systems (NeurIPS)*, 2021.

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

**Spotlight***Advances in Neural Information Processing Systems (NeurIPS)*, 2021.

- Variational Bayesian reinforcement learning with regret bounds
B. O'Donoghue

*Advances in Neural Information Processing Systems (NeurIPS)*, 2021.

- 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

*Advances in Neural Information Processing Systems (NeurIPS)*, 2021.

- Matrix games with bandit feedback
B. O'Donoghue, T. Lattimore, and I. Osband

*Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence*, (UAI), 2021.

- 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

**Spotlight***Proceedings of the International Conference on Learning Representations (ICLR)*, 2021.

- Sample Efficient Reinforcement Learning with REINFORCE
J. Zhang, J. Kim, B. O'Donoghue, and S. Boyd

*Proceedings of the AAAI Conference on Artificial Intelligence*, 35(12), 10887-10895, 2021.

- Making sense of reinforcement learning and probabilistic inference
B. O'Donoghue, I Osband, and C. Ionescu

**Spotlight***Proceedings of the International Conference on Learning Representations (ICLR)*, 2020.

- Hamiltonian descent for composite objectives
B. O'Donoghue and C. J. Maddison

*Advances in Neural Information Processing Systems (NeurIPS)*, 2019.

- Visualizations of decision regions in the presence of adversarial examples
G. Swirszcz, B. O'Donoghue, and P. Kohli

*Debugging Machine Learning Models Workshop*, ICLR, 2019.

- 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

*Proceedings of the International Conference on Learning Representations (ICLR)*, 2019.

- Adversarial risk and the dangers of evaluating against weak attacks
J. Uesato, B. O'Donoghue, A. van den Oord, and P. Kohli

*Proceedings of the International Conference on Machine Learning (ICML)*, pp. 5025-5034, 2018.

- The uncertainty Bellman equation and exploration
B. O'Donoghue, I. Osband, R. Munos, and V. Mnih

**Oral***Proceedings of the International Conference on Machine Learning (ICML)*, pp. 3836-3845. 2018.

- Combining policy gradient and Q-learning
B. O'Donoghue, R. Munos, K. Kavukcuoglu, and V. Mnih

*Proceedings of the International Conference on Learning Representations (ICLR)*, 2017.

- Iterated approximate value functions
B. O'Donoghue, Y. Wang, and S. Boyd

*Proceedings European Control Conference*, pp. 3882-3888, Zurich, July 2013.

- Min-max approximate dynamic programming
B. O'Donoghue, Y. Wang, and S. Boyd

*Proceedings IEEE Multi-Conference on Systems and Control*, pp. 424-431, September 2011.

- On the connection between Bregman divergence and value in regularized Markov decision processes
B. O'Donoghue

Technical note, 2022.

- 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.*

(* Equal contribution). In submission, 2021.

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

Working draft, 2021.

- 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

Working draft, 2018.

- Hamiltonian descent methods
C. J. Maddison, D. Paulin, Y. W. Teh, B. O'Donoghue, and A. Doucet

Working draft, 2018.

- Training verified learners with learned verifiers
K. (Dj) Dvijotham, S. Gowal, R. Stanforth, R. Arandjelovic, B. O'Donoghue, J. Uesato, and P. Kohli

Working draft, 2018.

- A primal-dual operator splitting method for conic optimization
E. Chu, B. O'Donoghue, N. Parikh, and S. Boyd

*Stanford internal report*, (2013).

- Suboptimal control policies via convex optimization
B. O'Donoghue