Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalization bounds for learning under graph-dependence: A survey   Traditional statistical learning theory relies on the assumption that data
are identically and independently generated from a given distribution (i.i.d.).
The independently distributed assumption, on the other hand, fails to hold in
many real applications. In this survey, we consider learning settings in which
examples are dependent and their dependence relationship can be characterized
by a graph. We collect various graph-dependent concentration bounds, which are
then used to derive Rademacher and stability generalization bounds for learning
from graph-dependent data. We illustrate this paradigm with three learning
tasks and provide some research directions for future work. To the best of our
knowledge, this is the first survey on this subject.

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The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin