Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

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, A. Tacchetti, T. Eccles, and J. Kramár. AAMAS, page 507-515. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Roland Gemperlein

Otto Gempt

Gudrun Gemperlein-Tegner

Franz Gemperle

Dieter Gemperlein

Other publications of authors with the same name

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning.K. McKee, I. Gemp, B. McWilliams, E. Duéñez-Guzmán, E. Hughes, and J. Leibo. AAMAS, page 869-877. International Foundation for Autonomous Agents and Multiagent Systems, (2020)Unmixing in the presence of nuisances with deep generative models.M. Parente, I. Gemp, and I. Durugkar. IGARSS, page 5189-5192. IEEE, (2017)Modeling Context in Cognition Using Variational Inequalities.I. Gemp, and S. Mahadevan. AAAI Fall Symposia, AAAI Press, (2014)Automated Data Cleansing through Meta-Learning.I. Gemp, G. Theocharous, and M. Ghavamzadeh. AAAI, page 4760-4761. AAAI Press, (2017)Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering.E. van der Pol, I. Gemp, and Y. Bachrach. TAG-ML, volume 196 of Proceedings of Machine Learning Research, page 304-311. PMLR, (2022)Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers.L. Marris, I. Gemp, T. Anthony, A. Tacchetti, S. Liu, and K. Tuyls. NeurIPS, (2022)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, M. Lanctot, J. Pérolat, R. Everett and 3 other author(s). NeurIPS, (2020)A Neural Network Auction For Group Decision Making Over a Continuous Space.Y. Bachrach, I. Gemp, M. Garnelo, J. Kramár, T. Eccles, D. Rosenbaum, and T. Graepel. IJCAI, page 4976-4979. ijcai.org, (2021)Generative Adversarial Equilibrium Solvers.D. Goktas, D. Parkes, I. Gemp, L. Marris, G. Piliouras, R. Elie, G. Lever, and A. Tacchetti. CoRR, (2023)Solving Large Sustainable Supply Chain Networks Using Variational Inequalities.I. Gemp, S. Mahadevan, and B. Liu. AAAI Workshop: Computational Sustainability, volume WS-15-06 of AAAI Technical Report, AAAI Press, (2015)

BibSonomy

Disambiguation of "Gemp, Ian"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent.

Please choose a person to relate this publication to

Roland Gemperlein

Otto Gempt

Gudrun Gemperlein-Tegner

Franz Gemperle

Dieter Gemperlein

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Gemp, Ian"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent.

Please choose a person to relate this publication to

Roland Gemperlein

Otto Gempt

Gudrun Gemperlein-Tegner

Franz Gemperle

Dieter Gemperlein

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent.