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ORCIDBrendan O'Donoghue
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2020 – today
- 2023
[j9]Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy:
POMRL: No-Regret Learning-to-Plan with Increasing Horizons. Trans. Mach. Learn. Res. 2023 (2023)- [c20]Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy:
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. ICML 2023: 25303-25336 - [c19]Brendan O'Donoghue:
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization. ICML 2023: 26382-26402 - [c18]Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh:
Optimistic Meta-Gradients. NeurIPS 2023 - [c17]Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue:
Probabilistic Inference in Reinforcement Learning Done Right. NeurIPS 2023 - [i24]Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh:
Optimistic Meta-Gradients. CoRR abs/2301.03236 (2023) - [i23]Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy:
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. CoRR abs/2302.01275 (2023) - [i22]Brendan O'Donoghue:
Efficient exploration via epistemic-risk-seeking policy optimization. CoRR abs/2302.09339 (2023) - [i21]Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue:
Probabilistic Inference in Reinforcement Learning Done Right. CoRR abs/2311.13294 (2023) - 2022
- [c16]Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy:
The Neural Testbed: Evaluating Joint Predictions. NeurIPS 2022 - [i20]Brendan O'Donoghue:
On the connection between Bregman divergence and value in regularized Markov decision processes. CoRR abs/2210.12160 (2022) - [i19]Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy:
POMRL: No-Regret Learning-to-Plan with Increasing Horizons. CoRR abs/2212.14530 (2022) - 2021
- [j8]Brendan O'Donoghue:
Operator Splitting for a Homogeneous Embedding of the Linear Complementarity Problem. SIAM J. Optim. 31(3): 1999-2023 (2021) - [c15]Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen P. Boyd:
Sample Efficient Reinforcement Learning with REINFORCE. AAAI 2021: 10887-10895 - [c14]Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. ICLR 2021 - [c13]Brendan O'Donoghue, Tor Lattimore:
Variational Bayesian Optimistic Sampling. NeurIPS 2021: 12507-12519 - [c12]David L. Applegate, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy:
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient. NeurIPS 2021: 20243-20257 - [c11]Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh:
Reward is enough for convex MDPs. NeurIPS 2021: 25746-25759 - [c10]Brendan O'Donoghue:
Variational Bayesian Reinforcement Learning with Regret Bounds. NeurIPS 2021: 28208-28221 - [c9]Brendan O'Donoghue, Tor Lattimore, Ian Osband:
Matrix games with bandit feedback. UAI 2021: 279-289 - [i18]Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. CoRR abs/2102.04323 (2021) - [i17]Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh:
Reward is enough for convex MDPs. CoRR abs/2106.00661 (2021) - [i16]Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh:
Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR abs/2106.00669 (2021) - [i15]Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy:
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? CoRR abs/2110.04629 (2021) - [i14]Brendan O'Donoghue, Tor Lattimore:
Variational Bayesian Optimistic Sampling. CoRR abs/2110.15688 (2021) - 2020
- [j7]Junzi Zhang
, Brendan O'Donoghue, Stephen P. Boyd:
Globally Convergent Type-I Anderson Acceleration for Nonsmooth Fixed-Point Iterations. SIAM J. Optim. 30(4): 3170-3197 (2020) - [c8]Brendan O'Donoghue, Ian Osband, Catalin Ionescu:
Making Sense of Reinforcement Learning and Probabilistic Inference. ICLR 2020 - [i13]Brendan O'Donoghue, Ian Osband, Catalin Ionescu:
Making Sense of Reinforcement Learning and Probabilistic Inference. CoRR abs/2001.00805 (2020) - [i12]Brendan O'Donoghue, Tor Lattimore, Ian Osband:
Stochastic matrix games with bandit feedback. CoRR abs/2006.05145 (2020) - [i11]Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen P. Boyd:
Sample Efficient Reinforcement Learning with REINFORCE. CoRR abs/2010.11364 (2020) - [i10]Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols:
Solving Mixed Integer Programs Using Neural Networks. CoRR abs/2012.13349 (2020)
2010 – 2019
- 2019
- [c7]Chongli Qin, Krishnamurthy (Dj) Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Verification of Non-Linear Specifications for Neural Networks. ICLR (Poster) 2019 - [c6]Brendan O'Donoghue, Chris J. Maddison:
Hamiltonian descent for composite objectives. NeurIPS 2019: 14443-14453 - [i9]Chongli Qin, Krishnamurthy (Dj) Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Verification of Non-Linear Specifications for Neural Networks. CoRR abs/1902.09592 (2019) - 2018
- [c5]Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih:
The Uncertainty Bellman Equation and Exploration. ICML 2018: 3836-3845 - [c4]Jonathan Uesato, Brendan O'Donoghue, Pushmeet Kohli, Aäron van den Oord:
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. ICML 2018: 5032-5041 - [i8]Jonathan Uesato, Brendan O'Donoghue, Aäron van den Oord, Pushmeet Kohli:
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. CoRR abs/1802.05666 (2018) - [i7]Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O'Donoghue, Jonathan Uesato, Pushmeet Kohli:
Training verified learners with learned verifiers. CoRR abs/1805.10265 (2018) - [i6]Brendan O'Donoghue:
Variational Bayesian Reinforcement Learning with Regret Bounds. CoRR abs/1807.09647 (2018) - [i5]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet:
Hamiltonian Descent Methods. CoRR abs/1809.05042 (2018) - [i4]Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles. CoRR abs/1811.09300 (2018) - 2017
- [c3]Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih:
Combining policy gradient and Q-learning. ICLR (Poster) 2017 - [i3]Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih:
The Uncertainty Bellman Equation and Exploration. CoRR abs/1709.05380 (2017) - 2016
- [j6]Brendan O'Donoghue, Eric Chu, Neal Parikh, Stephen P. Boyd:
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding. J. Optim. Theory Appl. 169(3): 1042-1068 (2016) - [i2]Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih:
PGQ: Combining policy gradient and Q-learning. CoRR abs/1611.01626 (2016) - 2015
- [j5]Brendan O'Donoghue, Emmanuel J. Candès:
Adaptive Restart for Accelerated Gradient Schemes. Found. Comput. Math. 15(3): 715-732 (2015) - [j4]Yuanming Shi, Jun Zhang, Brendan O'Donoghue, Khaled Ben Letaief:
Large-Scale Convex Optimization for Dense Wireless Cooperative Networks. IEEE Trans. Signal Process. 63(18): 4729-4743 (2015) - [i1]Yuanming Shi, Jun Zhang, Brendan O'Donoghue, Khaled Ben Letaief:
Large-Scale Convex Optimization for Dense Wireless Cooperative Networks. CoRR abs/1506.00749 (2015) - 2014
- [j3]Stephen P. Boyd, Mark T. Müller, Brendan O'Donoghue, Yang Wang:
Performance Bounds and Suboptimal Policies for Multi-Period Investment. Found. Trends Optim. 1(1): 1-72 (2014) - [j2]Tom Goldstein, Brendan O'Donoghue, Simon Setzer, Richard G. Baraniuk:
Fast Alternating Direction Optimization Methods. SIAM J. Imaging Sci. 7(3): 1588-1623 (2014) - 2013
- [j1]Brendan O'Donoghue, Giorgos Stathopoulos, Stephen P. Boyd:
A Splitting Method for Optimal Control. IEEE Trans. Control. Syst. Technol. 21(6): 2432-2442 (2013) - [c2]Brendan O'Donoghue, Yang Wang, Stephen P. Boyd:
Iterated approximate value functions. ECC 2013: 3882-3888 - 2011
- [c1]Brendan O'Donoghue, Yang Wang, Stephen P. Boyd:
Min-max approximate dynamic programming. CACSD 2011: 424-431
Coauthor Index
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