Generative multi-adversarial networks I Durugkar, I Gemp, S Mahadevan International Conference on Learning Representations, 2017 | 456 | 2017 |
Social diversity and social preferences in mixed-motive reinforcement learning KR McKee, I Gemp, B McWilliams, EA Duéñez-Guzmán, E Hughes, ... arXiv preprint arXiv:2002.02325, 2020 | 89 | 2020 |
Proximal reinforcement learning: A new theory of sequential decision making in primal-dual spaces S Mahadevan, B Liu, P Thomas, W Dabney, S Giguere, N Jacek, I Gemp, ... arXiv preprint arXiv:1405.6757, 2014 | 69 | 2014 |
Global convergence to the equilibrium of gans using variational inequalities I Gemp, S Mahadevan arXiv preprint arXiv:1808.01531, 2018 | 56 | 2018 |
Eigengame: PCA as a nash equilibrium I Gemp, B McWilliams, C Vernade, T Graepel arXiv preprint arXiv:2010.00554, 2020 | 52 | 2020 |
Learning to play no-press diplomacy with best response policy iteration T Anthony, T Eccles, A Tacchetti, J Kramár, I Gemp, T Hudson, N Porcel, ... Advances in Neural Information Processing Systems 33, 17987-18003, 2020 | 52 | 2020 |
Quantitative analysis of synaptic release at the photoreceptor synapse G Duncan, K Rabl, I Gemp, R Heidelberger, WB Thoreson Biophysical journal 98 (10), 2102-2110, 2010 | 44 | 2010 |
Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy J Kramár, T Eccles, I Gemp, A Tacchetti, KR McKee, M Malinowski, ... Nature Communications 13 (1), 7214, 2022 | 40 | 2022 |
Cadherin-dependent cell morphology in an epithelium: constructing a quantitative dynamical model IM Gemp, RW Carthew, S Hilgenfeldt PLoS computational biology 7 (7), e1002115, 2011 | 22 | 2011 |
Game-theoretic vocabulary selection via the shapley value and banzhaf index R Patel, M Garnelo, I Gemp, C Dyer, Y Bachrach Proceedings of the 2021 Conference of the North American Chapter of the …, 2021 | 19 | 2021 |
Sample-based approximation of Nash in large many-player games via gradient descent I Gemp, R Savani, M Lanctot, Y Bachrach, T Anthony, R Everett, ... arXiv preprint arXiv:2106.01285, 2021 | 18 | 2021 |
Automated data cleansing through meta-learning I Gemp, G Theocharous, M Ghavamzadeh Proceedings of the AAAI Conference on Artificial Intelligence 31 (2), 4760-4761, 2017 | 18 | 2017 |
Smooth markets: A basic mechanism for organizing gradient-based learners D Balduzzi, WM Czarnecki, TW Anthony, IM Gemp, E Hughes, JZ Leibo, ... arXiv preprint arXiv:2001.04678, 2020 | 17 | 2020 |
D3c: Reducing the price of anarchy in multi-agent learning I Gemp, KR McKee, R Everett, EA Duéñez-Guzmán, Y Bachrach, ... arXiv preprint arXiv:2010.00575, 2020 | 16 | 2020 |
Combining tree-search, generative models, and Nash bargaining concepts in game-theoretic reinforcement learning Z Li, M Lanctot, KR McKee, L Marris, I Gemp, D Hennes, P Muller, ... arXiv preprint arXiv:2302.00797, 2023 | 14 | 2023 |
Proximal gradient temporal difference learning: Stable reinforcement learning with polynomial sample complexity B Liu, I Gemp, M Ghavamzadeh, J Liu, S Mahadevan, M Petrik Journal of Artificial Intelligence Research 63, 461-494, 2018 | 14 | 2018 |
Turbocharging solution concepts: Solving NEs, CEs and CCEs with neural equilibrium solvers L Marris, I Gemp, T Anthony, A Tacchetti, S Liu, K Tuyls Advances in Neural Information Processing Systems 35, 5586-5600, 2022 | 13 | 2022 |
Feature likelihood score: Evaluating the generalization of generative models using samples M Jiralerspong, J Bose, I Gemp, C Qin, Y Bachrach, G Gidel Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
Eigengame unloaded: When playing games is better than optimizing I Gemp, B McWilliams, C Vernade, T Graepel arXiv preprint arXiv:2102.04152, 2021 | 10 | 2021 |
States as strings as strategies: Steering language models with game-theoretic solvers I Gemp, Y Bachrach, M Lanctot, R Patel, V Dasagi, L Marris, G Piliouras, ... arXiv preprint arXiv:2402.01704, 2024 | 9 | 2024 |