Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: It's Not What Machines Can Learn, It's What We Cannot Teach   Can deep neural networks learn to solve any task, and in particular problems
of high complexity? This question attracts a lot of interest, with recent works
tackling computationally hard tasks such as the traveling salesman problem and
satisfiability. In this work we offer a different perspective on this question.
Given the common assumption that $\textit{NP} \neq \textit{coNP}$ we prove that
any polynomial-time sample generator for an $\textit{NP}$-hard problem samples,
in fact, from an easier sub-problem. We empirically explore a case study,
Conjunctive Query Containment, and show how common data generation techniques
generate biased datasets that lead practitioners to over-estimate model
accuracy. Our results suggest that machine learning approaches that require
training on a dense uniform sampling from the target distribution cannot be
used to solve computationally hard problems, the reason being the difficulty of
generating sufficiently large and unbiased training sets.

9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol