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| -rw-r--r-- | posts/2019-03-13-a-tail-of-two-densities.md | 2 |
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diff --git a/posts/2019-03-13-a-tail-of-two-densities.md b/posts/2019-03-13-a-tail-of-two-densities.md index 460364a..f3e409c 100644 --- a/posts/2019-03-13-a-tail-of-two-densities.md +++ b/posts/2019-03-13-a-tail-of-two-densities.md @@ -8,7 +8,7 @@ comments: true This is Part 1 of a two-part post where I give an introduction to differential privacy, which is a study of tail bounds of the divergence between probability measures, with the end goal of applying it to stochastic -gradient descent. +gradient descent. I start with the definition of $\epsilon$-differential privacy (corresponding to max divergence), followed by |