Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Rates for Distributed Learning with Random Features   In recent studies, the generalization properties for distributed learning and
random features assumed the existence of the target concept over the hypothesis
space. However, this strict condition is not applicable to the more common
non-attainable case. In this paper, using refined proof techniques, we first
extend the optimal rates for distributed learning with random features to the
non-attainable case. Then, we reduce the number of required random features via
data-dependent generating strategy, and improve the allowed number of
partitions with additional unlabeled data. Theoretical analysis shows these
techniques remarkably reduce computational cost while preserving the optimal
generalization accuracy under standard assumptions. Finally, we conduct several
experiments on both simulated and real-world datasets, and the empirical
results validate our theoretical findings.

29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin