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ORCIDIan Gemp
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- affiliation: DeepMind, London, UK
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2020 – today
- 2024
[j4]Zheng Yu
, Junyu Zhang, Zheng Wen, Andrea Tacchetti, Mengdi Wang, Ian Gemp:
Teamwork Reinforcement Learning With Concave Utilities. IEEE Trans. Mob. Comput. 23(5): 5709-5721 (2024)- [c24]Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao, Edgar A. Duéñez-Guzmán, Sarah Perrin, Andras Gyorgy, Romuald Elie, Georgios Piliouras, Michael Kaisers, Daniel Hennes, Kalesha Bullard, Kate Larson, Yoram Bachrach:
Approximating the Core via Iterative Coalition Sampling. AAMAS 2024: 669-678 - [c23]Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess:
NfgTransformer: Equivariant Representation Learning for Normal-form Games. ICLR 2024 - [c22]Ian Gemp, Luke Marris, Georgios Piliouras:
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization. ICLR 2024 - [c21]Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, Andrea Tacchetti:
Generative Adversarial Equilibrium Solvers. ICLR 2024 - [i30]Ian Gemp, Yoram Bachrach, Marc Lanctot, Roma Patel, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, Siqi Liu, Karl Tuyls:
States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers. CoRR abs/2402.01704 (2024) - [i29]Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao, Edgar A. Duéñez-Guzmán, Sarah Perrin, Andras Gyorgy, Romuald Elie, Georgios Piliouras, Michael Kaisers, Daniel Hennes, Kalesha Bullard, Kate Larson, Yoram Bachrach:
Approximating the Core via Iterative Coalition Sampling. CoRR abs/2402.03928 (2024) - [i28]Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess:
NfgTransformer: Equivariant Representation Learning for Normal-form Games. CoRR abs/2402.08393 (2024) - [i27]Luke Marris, Ian Gemp, Siqi Liu, Joel Z. Leibo, Georgios Piliouras:
Visualizing 2x2 Normal-Form Games: twoxtwogame LaTeX Package. CoRR abs/2402.16985 (2024) - [i26]Ian Gemp, Andreas Haupt, Luke Marris, Siqi Liu, Georgios Piliouras:
Convex Markov Games: A Framework for Fairness, Imitation, and Creativity in Multi-Agent Learning. CoRR abs/2410.16600 (2024) - [i25]Marc Lanctot, Kate Larson, Michael Kaisers, Quentin Berthet, Ian Gemp, Manfred Diaz, Roberto-Rafael Maura-Rivero, Yoram Bachrach, Anna Koop, Doina Precup:
Soft Condorcet Optimization for Ranking of General Agents. CoRR abs/2411.00119 (2024) - 2023
- [c20]Kevin Du, Ian Gemp, Yi Wu, Yingying Wu:
AlphaSnake: Policy Iteration on a Nondeterministic NP-Hard Markov Decision Process (Student Abstract). AAAI 2023: 16204-16205 - [c19]Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Kate Larson, Yoram Bachrach, Michael P. Wellman, Paul Muller:
Search-Improved Game-Theoretic Multiagent Reinforcement Learning in General and Negotiation Games. AAMAS 2023: 2445-2447 - [c18]Ian Gemp, Charlie Chen, Brian McWilliams:
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. ICLR 2023 - [c17]Marco Jiralerspong, Avishek Joey Bose, Ian Gemp, Chongli Qin, Yoram Bachrach, Gauthier Gidel:
Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples. NeurIPS 2023 - [i24]Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman:
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning. CoRR abs/2302.00797 (2023) - [i23]Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, Andrea Tacchetti:
Generative Adversarial Equilibrium Solvers. CoRR abs/2302.06607 (2023) - [i22]Luke Marris, Ian Gemp, Georgios Piliouras:
Equilibrium-Invariant Embedding, Metric Space, and Fundamental Set of 2×2 Normal-Form Games. CoRR abs/2304.09978 (2023) - [i21]Ian Gemp, Luke Marris, Georgios Piliouras:
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization. CoRR abs/2310.06689 (2023) - 2022
- [j3]Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls:
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35(4): 271-284 (2022) - [c16]Ian Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti:
D3C: Reducing the Price of Anarchy in Multi-Agent Learning. AAMAS 2022: 498-506 - [c15]Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár:
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS 2022: 507-515 - [c14]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame Unloaded: When playing games is better than optimizing. ICLR 2022 - [c13]Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls:
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. NeurIPS 2022 - [c12]Elise van der Pol, Ian Gemp, Yoram Bachrach:
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering. TAG-ML 2022: 304-311 - [i20]Ian Gemp, Charlie Chen, Brian McWilliams:
The Generalized Eigenvalue Problem as a Nash Equilibrium. CoRR abs/2206.04993 (2022) - [i19]Elise van der Pol, Ian Gemp, Yoram Bachrach, Richard Everett:
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering. CoRR abs/2207.14589 (2022) - [i18]Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls:
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR abs/2209.10958 (2022) - [i17]Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome T. Connor, Karl Tuyls, Thore Graepel:
Game Theoretic Rating in N-player general-sum games with Equilibria. CoRR abs/2210.02205 (2022) - [i16]Luke Marris, Ian Gemp, Thomas W. Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls:
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. CoRR abs/2210.09257 (2022) - [i15]Kevin Du, Ian Gemp, Yi Wu, Yingying Wu:
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process. CoRR abs/2211.09622 (2022) - 2021
- [c11]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. ICLR 2021 - [c10]Yoram Bachrach, Ian Gemp, Marta Garnelo, János Kramár, Tom Eccles, Dan Rosenbaum, Thore Graepel:
A Neural Network Auction For Group Decision Making Over a Continuous Space. IJCAI 2021: 4976-4979 - [c9]Roma Patel, Marta Garnelo, Ian Gemp, Chris Dyer, Yoram Bachrach:
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index. NAACL-HLT 2021: 2789-2798 - [i14]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame Unloaded: When playing games is better than optimizing. CoRR abs/2102.04152 (2021) - [i13]Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár:
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR abs/2106.01285 (2021) - 2020
- [c8]Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo:
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. AAMAS 2020: 869-877 - [c7]David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel:
Smooth markets: A basic mechanism for organizing gradient-based learners. ICLR 2020 - [c6]Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS 2020 - [i12]David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel:
Smooth markets: A basic mechanism for organizing gradient-based learners. CoRR abs/2001.04678 (2020) - [i11]Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo:
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. CoRR abs/2002.02325 (2020) - [i10]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. CoRR abs/2006.03976 (2020) - [i9]Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR abs/2006.04635 (2020) - [i8]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. CoRR abs/2010.00554 (2020) - [i7]Ian Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti:
D3C: Reducing the Price of Anarchy in Multi-Agent Learning. CoRR abs/2010.00575 (2020)
2010 – 2019
- 2018
- [j2]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. J. Artif. Intell. Res. 63: 461-494 (2018) - [i6]Ian Gemp, Sridhar Mahadevan:
Global Convergence to the Equilibrium of GANs using Variational Inequalities. CoRR abs/1808.01531 (2018) - 2017
- [c5]Ian Gemp, Georgios Theocharous, Mohammad Ghavamzadeh:
Automated Data Cleansing through Meta-Learning. AAAI 2017: 4760-4761 - [c4]Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan:
Generative Multi-Adversarial Networks. ICLR (Poster) 2017 - [c3]Mario Parente, Ian Gemp, Ishan P. Durugkar:
Unmixing in the presence of nuisances with deep generative models. IGARSS 2017: 5189-5192 - [i5]Ian Gemp, Sridhar Mahadevan:
Online Monotone Games. CoRR abs/1710.07328 (2017) - 2016
- [i4]Ian Gemp, Ishan P. Durugkar, Mario Parente, Melinda Darby Dyar, Sridhar Mahadevan:
Deep Generative Models for Spectroscopic Analysis on Mars. CoRR abs/1608.05983 (2016) - [i3]Ian Gemp, Sridhar Mahadevan:
Online Monotone Optimization. CoRR abs/1608.07888 (2016) - [i2]Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan:
Generative Multi-Adversarial Networks. CoRR abs/1611.01673 (2016) - 2015
- [c2]Ian Gemp, Sridhar Mahadevan, Bo Liu:
Solving Large Sustainable Supply Chain Networks Using Variational Inequalities. AAAI Workshop: Computational Sustainability 2015 - 2014
- [c1]Ian Gemp, Sridhar Mahadevan:
Modeling Context in Cognition Using Variational Inequalities. AAAI Fall Symposia 2014 - [i1]Sridhar Mahadevan, Bo Liu, Philip S. Thomas, William Dabney, Stephen Giguere, Nicholas Jacek, Ian Gemp, Ji Liu:
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces. CoRR abs/1405.6757 (2014) - 2011
- [j1]Ian M. Gemp, Richard W. Carthew, Sascha Hilgenfeldt:
Cadherin-Dependent Cell Morphology in an Epithelium: Constructing a Quantitative Dynamical Model. PLoS Comput. Biol. 7(7) (2011)
Coauthor Index
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