| Deepstack: Expert-level artificial intelligence in heads-up no-limit poker M Moravčík, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ... Science 356 (6337), 508-513, 2017 | 1057 | 2017 |
| Text understanding with the attention sum reader network R Kadlec, M Schmid, O Bajgar, J Kleindienst arXiv preprint arXiv:1603.01547, 2016 | 340 | 2016 |
| Improved deep learning baselines for ubuntu corpus dialogs R Kadlec, M Schmid, J Kleindienst arXiv preprint arXiv:1510.03753, 2015 | 132 | 2015 |
| Variance reduction in monte carlo counterfactual regret minimization (VR-MCCFR) for extensive form games using baselines M Schmid, N Burch, M Lanctot, M Moravcik, R Kadlec, M Bowling Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2157-2164, 2019 | 69 | 2019 |
| Rethinking formal models of partially observable multiagent decision making V Kovařík, M Schmid, N Burch, M Bowling, V Lisý Artificial Intelligence 303, 103645, 2022 | 59 | 2022 |
| Refining subgames in large imperfect information games M Moravcik, M Schmid, K Ha, M Hladik, S Gaukrodger Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 44 | 2016 |
| Player of games M Schmid, M Moravcik, N Burch, R Kadlec, J Davidson, K Waugh, N Bard, ... arXiv preprint arXiv:2112.03178, 2021 | 43 | 2021 |
| Revisiting CFR+ and alternating updates N Burch, M Moravcik, M Schmid Journal of Artificial Intelligence Research 64, 429-443, 2019 | 42 | 2019 |
| Automatic question generation from natural text A Kantor, J Kleindienst, M Schmid US Patent 9,904,675, 2018 | 31 | 2018 |
| Deepstack: expert-level artificial intelligence in no-limit poker. CoRR abs/1701.01724 (2017) M Moravcík, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ... arXiv preprint arXiv:1701.01724, 2017 | 25 | 2017 |
| Approximate exploitability: Learning a best response in large games F Timbers, N Bard, E Lockhart, M Lanctot, M Schmid, N Burch, ... arXiv preprint arXiv:2004.09677, 2020 | 23 | 2020 |
| 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 | 20 | 2020 |
| Aivat: A new variance reduction technique for agent evaluation in imperfect information games N Burch, M Schmid, M Moravcik, D Morill, M Bowling Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 17 | 2018 |
| Low-variance and zero-variance baselines for extensive-form games T Davis, M Schmid, M Bowling International Conference on Machine Learning, 2392-2401, 2020 | 16 | 2020 |
| Bounding the support size in extensive form games with imperfect information M Schmid, M Moravcik, M Hladik Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 14 | 2014 |
| 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 | 12 | 2021 |
| Search in Imperfect Information Games M Schmid arXiv preprint arXiv:2111.05884, 2021 | 10 | 2021 |
| Sound search in imperfect information games M Šustr, M Schmid, M Moravčík, N Burch, M Lanctot, M Bowling arXiv preprint arXiv:2006.08740, 2020 | 9* | 2020 |
| Approximate exploitability: learning a best response F Timbers, N Bard, E Lockhart, M Lanctot, M Schmid, N Burch, ... Proceedings of the International Joint Conference on Artificial Intelligence …, 2022 | 6 | 2022 |
| Multiple-point cognitive identity challenge system DS Anderson, OCW Blodgett, T Durniak, MR Moore, M Schmid US Patent 10,210,317, 2019 | 5 | 2019 |
Michael BowlingUniversity of AlbertaVerified email at ualberta.ca
