Finbarr Timbers
Finbarr Timbers
Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Detecting duplicate bug reports with software engineering domain knowledge
K Aggarwal, F Timbers, T Rutgers, A Hindle, E Stroulia, R Greiner
Journal of Software: Evolution and Process 29 (3), e1821, 2017
652017
OpenSpiel: A framework for reinforcement learning in games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
592019
Computing approximate equilibria in sequential adversarial games by exploitability descent
E Lockhart, M Lanctot, J Pérolat, JB Lespiau, D Morrill, F Timbers, K Tuyls
arXiv preprint arXiv:1903.05614, 2019
352019
Bug reports dataset
A Alipour, A Hindle, T Rutgers, R Dawson, F Timbers, K Aggarwal
92013
OpenSpiel: A Framework for Reinforcement Learning in Games. CoRR abs/1908.09453 (2019)
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint cs.LG/1908.09453, 2019
82019
The advantage regret-matching actor-critic
A Gruslys, M Lanctot, R Munos, F Timbers, M Schmid, J Perolat, D Morrill, ...
arXiv preprint arXiv:2008.12234, 2020
42020
Approximate exploitability: Learning a best response in large games
F Timbers, E Lockhart, M Lanctot, M Schmid, J Schrittwieser, T Hubert, ...
arXiv preprint arXiv:2004.09677, 2020
42020
Solving Common-Payoff Games with Approximate Policy Iteration
S Sokota
12020
Solving Common-Payoff Games with Approximate Policy Iteration
S Sokota, E Lockhart, F Timbers, E Davoodi, R D'Orazio, N Burch, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9695-9703, 2021
2021
Fast Computation of Nash Equilibria in Imperfect Information Games
R Munos, J Perolat, JB Lespiau, M Rowland, B De Vylder, M Lanctot, ...
International Conference on Machine Learning, 7119-7129, 2020
2020
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