It turns out people have researched how to do algorithmic recommendations without users having to reveal their personal preferences, and I am intrigued. Apparently, in principle we could have the good parts of, say, Netflix suggesting more things you might want to watch, without exposing ourselves to entities like Facebook selling all our data.
See "Distributed Differential Privacy and Applications" by Narayan, for example. (Also that's the first CC-BY licensed PhD thesis I've seen!)
@b_cavello I still don't see the relation, but I agree that the use of adversarial networks to limit over-training was a really interesting part of that talk. I've seen stuff before about trying to remove bias from word2vec embeddings so that for example "doctor" doesn't get associated to "man" and "nurse" doesn't get associated to "woman", and I could imagine using the GAN approach to try to tackle that kind of problem too.
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