  Regression is typically treated as a curve-fitting process where the goal is
to fit a prediction function to data. With the help of conditional generative
adversarial networks, we propose to solve this age-old problem in a different
way; we aim to learn a prediction function whose outputs, when paired with the
corresponding inputs, are indistinguishable from feature-label pairs in the
training dataset. We show that this approach to regression makes fewer
assumptions on the distribution of the data we are fitting to and, therefore,
has better representation capabilities. We draw parallels with generalized
linear models in statistics and show how our proposal serves as an extension of
them to neural networks. We demonstrate the superiority of this new approach to
standard regression with experiments on multiple synthetic and publicly
available real-world datasets, finding encouraging results, especially with
real-world heavy-tailed regression datasets. To make our work more
reproducible, we release our source code. Link to repository:
https://anonymous.4open.science/r/regressGAN-7B71/
