Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Private-K-Selection with Adaptive K and Application to
  Multi-label PATE   We provide an end-to-end Renyi DP based-framework for differentially private
top-$k$ selection. Unlike previous approaches, which require a data-independent
choice on $k$, we propose to privately release a data-dependent choice of $k$
such that the gap between $k$-th and the $(k+1)$st "quality" is large. This is
achieved by a novel application of the Report-Noisy-Max. Not only does this
eliminate one hyperparameter, the adaptive choice of $k$ also certifies the
stability of the top-$k$ indices in the unordered set so we can release them
using a variant of propose-test-release (PTR) without adding noise. We show
that our construction improves the privacy-utility trade-offs compared to the
previous top-$k$ selection algorithms theoretically and empirically.
Additionally, we apply our algorithm to "Private Aggregation of Teacher
Ensembles (PATE)" in multi-label classification tasks with a large number of
labels and show that it leads to significant performance gains.

16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga