Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via
  $f$-Divergences   We derive the optimal differential privacy (DP) parameters of a mechanism
that satisfies a given level of R\'enyi differential privacy (RDP). Our result
is based on the joint range of two $f$-divergences that underlie the
approximate and the R\'enyi variations of differential privacy. We apply our
result to the moments accountant framework for characterizing privacy
guarantees of stochastic gradient descent. When compared to the
state-of-the-art, our bounds may lead to about 100 more stochastic gradient
descent iterations for training deep learning models for the same privacy
budget.

36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga