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- W4383558110 abstract "Many multi-agent machine learning settings can be modeled as games, from social or economic systems with algorithmic decision-makers to popular learning architectures such as generative adversarial networks (GANs). Desired outcomes in these settings are often encoded as equilibrium concepts, and therefore a primary goal is identifying machine learning algorithms with provable convergence to these equilibria. However, a growing body of negative results casts doubt on this goal by uncovering games exhibiting non-convergence, chaos, and even essentially arbitrary behaviour [Andrade et al. 2021; Benaïm et al. 2012; Cheung and Piliouras 2019; Chotibut et al. 2020; Flokas et al. 2020; Letcher 2021; Milionis et al. 2022; Wibisono et al. 2022]." @default.
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- W4383558110 date "2023-07-07" @default.
- W4383558110 modified "2023-10-18" @default.
- W4383558110 title "No-Regret Learning in Games is Turing Complete" @default.
- W4383558110 doi "https://doi.org/10.1145/3580507.3597714" @default.
- W4383558110 hasPublicationYear "2023" @default.
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