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Smooth markets: A basic mechanism for organizing gradient-based learners

Balduzzi, D; Czarnecki, WM; Anthony, T; Gemp, IM; Hughes, E; Leibo, JZ; Piliouras, G; (2020) Smooth markets: A basic mechanism for organizing gradient-based learners. In: Proceedings of the 8th International Conference on Learning Representations, ICLR 2020. (pp. pp. 1-18). ICLR Green open access

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Abstract

With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes some GANs, adversarial training, and other recent algorithms. We show that SM-games are amenable to analysis and optimization using first-order methods.

Type: Proceedings paper
Title: Smooth markets: A basic mechanism for organizing gradient-based learners
Event: 8th International Conference on Learning Representations, ICLR 2020
Open access status: An open access version is available from UCL Discovery
Publisher version: https://openreview.net/forum?id=B1xMEerYvB
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: game theory, optimization, gradient descent, adversarial learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10109590
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