  We study the problem of discovering functional dependencies (FD) from a noisy
dataset. We focus on FDs that correspond to statistical dependencies in a
dataset and draw connections between FD discovery and structure learning in
probabilistic graphical models. We show that discovering FDs from a noisy
dataset is equivalent to learning the structure of a graphical model over
binary random variables, where each random variable corresponds to a functional
of the dataset attributes. We build upon this observation to introduce AutoFD a
conceptually simple framework in which learning functional dependencies
corresponds to solving a sparse regression problem. We show that our methods
can recover true functional dependencies across a diverse array of real-world
and synthetic datasets, even in the presence of noisy or missing data. We find
that AutoFD scales to large data instances with millions of tuples and hundreds
of attributes while it yields an average F1 improvement of 2 times against
state-of-the-art FD discovery methods.
