Adversarial learning

Statistics against Shayṭtān

General adversarial learning, where the noise is not purely random, but chosen to be the worst possible noise for you.

As renewed in fame recently by the related method of generative adversarial networks.

🏗 discuss politics implied by treating the learning as a battle with a conniving adversary as opposed to an uncaring universe, mention obvious connection with the theist neoreactionary zeitgeist. I’m sure someone has done this well in a terribly eloquent blog post, but I haven’t found one I’d want to link to yet.

Regardless of politically suggestive structure, application of game theory in the place of pure randomness is probably interesting in many areas although I don’t know most of them. Adversarial bandits is the obvious one in my world.



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Zhang, Rui, and Quanyan Zhu. 2017. “Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries.” October 12, 2017.

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