Ian Gemp
Cited by
Cited by
Generative multi-adversarial networks
I Durugkar, I Gemp, S Mahadevan
International Conference on Learning Representations, 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
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
Global convergence to the equilibrium of gans using variational inequalities
I Gemp, S Mahadevan
arXiv preprint arXiv:1808.01531, 2018
Eigengame: PCA as a nash equilibrium
I Gemp, B McWilliams, C Vernade, T Graepel
arXiv preprint arXiv:2010.00554, 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
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
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
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
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
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
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
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
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
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
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
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
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
Eigengame unloaded: When playing games is better than optimizing
I Gemp, B McWilliams, C Vernade, T Graepel
arXiv preprint arXiv:2102.04152, 2021
The unreasonable effectiveness of adam on cycles
I Gemp, B McWilliams
NeurIPS Workshop on Bridging Game Theory and Deep Learning, 2019
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