Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Formal Proof of PAC Learnability for Decision Stumps   We present a formal proof in Lean of probably approximately correct (PAC)
learnability of the concept class of decision stumps. This classic result in
machine learning theory derives a bound on error probabilities for a simple
type of classifier. Though such a proof appears simple on paper, analytic and
measure-theoretic subtleties arise when carrying it out fully formally. Our
proof is structured so as to separate reasoning about deterministic properties
of a learning function from proofs of measurability and analysis of
probabilities.

37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph