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
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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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