Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering Reliable Approximate Functional Dependencies   Given a database and a target attribute of interest, how can we tell whether
there exists a functional, or approximately functional dependence of the target
on any set of other attributes in the data? How can we reliably, without bias
to sample size or dimensionality, measure the strength of such a dependence?
And, how can we efficiently discover the optimal or $\alpha$-approximate
top-$k$ dependencies? These are exactly the questions we answer in this paper.
  As we want to be agnostic on the form of the dependence, we adopt an
information-theoretic approach, and construct a reliable, bias correcting score
that can be efficiently computed. Moreover, we give an effective optimistic
estimator of this score, by which for the first time we can mine the
approximate functional dependencies from data with guarantees of optimality.
Empirical evaluation shows that the derived score achieves a good bias for
variance trade-off, can be used within an efficient discovery algorithm, and
indeed discovers meaningful dependencies. Most important, it remains reliable
in the face of data sparsity.

20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics