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Ryan D'Orazio
Ryan D'Orazio
Mila, Université de Montréal
Verified email at mila.quebec - Homepage
Title
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Cited by
Year
A unified approach to reinforcement learning, quantal response equilibria, and two-player zero-sum games
S Sokota, R D'Orazio, JZ Kolter, N Loizou, M Lanctot, I Mitliagkas, ...
arXiv preprint arXiv:2206.05825, 2022
372022
Hindsight and sequential rationality of correlated play
D Morrill, R D'Orazio, R Sarfati, M Lanctot, JR Wright, AR Greenwald, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 5584-5594, 2021
332021
Efficient deviation types and learning for hindsight rationality in extensive-form games
D Morrill, R D’Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald
International Conference on Machine Learning, 7818-7828, 2021
322021
Stochastic mirror descent: Convergence analysis and adaptive variants via the mirror stochastic polyak stepsize
R D'Orazio, N Loizou, I Laradji, I Mitliagkas
arXiv preprint arXiv:2110.15412, 2021
262021
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
142021
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of -Regression Counterfactual Regret Minimization
R D'Orazio, D Morrill, JR Wright, M Bowling
arXiv preprint arXiv:1912.02967, 2019
112019
Simultaneous prediction intervals for patient-specific survival curves
S Sokota, R D'Orazio, K Javed, H Haider, R Greiner
arXiv preprint arXiv:1906.10780, 2019
72019
Regret minimization with function approximation in extensive-form games
R D'Orazio
62020
Optimistic and adaptive lagrangian hedging
R D'Orazio, R Huang
arXiv preprint arXiv:2101.09603, 2021
42021
On stochastic mirror descent: Convergence analysis and adaptive variants
R D’Orazio, N Loizou, I Laradji, I Mitliagkas
Beyond First-Order Methods in ML Systems Workshop, Int. Conf. Machine Learning, 2021
22021
Abstracting imperfect information away from two-player zero-sum games
S Sokota, R D’Orazio, CK Ling, DJ Wu, JZ Kolter, N Brown
International Conference on Machine Learning, 32169-32193, 2023
12023
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
D Morrill, R D'Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald
arXiv preprint arXiv:2205.12031, 2022
12022
Bounds for approximate regret-matching algorithms
R D'Orazio, D Morrill, JR Wright
arXiv preprint arXiv:1910.01706, 2019
12019
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games Supplementary
D Morrill, R D’Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald
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