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Ian Gemp
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- affiliation: DeepMind, London, UK
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2020 – today
- 2024
- [j4]Zheng Yu
, Junyu Zhang
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 - [i28]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) - [i27]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) - [i26]Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess:
NfgTransformer: Equivariant Representation Learning for Normal-form Games. CoRR abs/2402.08393 (2024) - [i25]Luke Marris, Ian Gemp, Siqi Liu, Joel Z. Leibo, Georgios Piliouras:
Visualizing 2x2 Normal-Form Games: twoxtwogame LaTeX Package. CoRR abs/2402.16985 (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
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
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
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
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
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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last updated on 2024-10-07 21:15 CEST by the dblp team
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