| Generative multi-adversarial networks I Durugkar, I Gemp, S Mahadevan International Conference on Learning Representations, 2017 | 388 | 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 | 55 | 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 | 55 | 2014 |
| Global convergence to the equilibrium of gans using variational inequalities I Gemp, S Mahadevan arXiv preprint arXiv:1808.01531, 2018 | 46 | 2018 |
| 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 | 41 | 2010 |
| Eigengame: PCA as a nash equilibrium I Gemp, B McWilliams, C Vernade, T Graepel arXiv preprint arXiv:2010.00554, 2020 | 36 | 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 | 33 | 2020 |
| 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 | 19 | 2011 |
| 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 | 13 | 2020 |
| 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 | 12 | 2018 |
| 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 | 12 | 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 | 11 | 2020 |
| 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 | 9 | 2021 |
| Eigengame unloaded: When playing games is better than optimizing I Gemp, B McWilliams, C Vernade, T Graepel arXiv preprint arXiv:2102.04152, 2021 | 9 | 2021 |
| The unreasonable effectiveness of adam on cycles I Gemp, B McWilliams NeurIPS Workshop on Bridging Game Theory and Deep Learning, 2019 | 9 | 2019 |
| 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 | 7 | 2021 |
| 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 | 6 | 2022 |
| Weakly semi-supervised neural topic models I Gemp, R Nallapati, R Ding, F Nan, B Xiang | 4 | 2019 |
| Online monotone games I Gemp, S Mahadevan arXiv preprint arXiv:1710.07328, 2017 | 4 | 2017 |
| Unmixing in the presence of nuisances with deep generative models M Parente, I Gemp, I Durugkar 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2017 | 4 | 2017 |
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, AmherstVerified email at cs.umass.edu
