Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Three Tools for Practical Differential Privacy   Differentially private learning on real-world data poses challenges for
standard machine learning practice: privacy guarantees are difficult to
interpret, hyperparameter tuning on private data reduces the privacy budget,
and ad-hoc privacy attacks are often required to test model privacy. We
introduce three tools to make differentially private machine learning more
practical: (1) simple sanity checks which can be carried out in a centralized
manner before training, (2) an adaptive clipping bound to reduce the effective
number of tuneable privacy parameters, and (3) we show that large-batch
training improves model performance.

12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
10
The categories of this paper are: q-bio