  Development of an accurate, flexible, and numerically efficient uncertainty
quantification (UQ) method is one of fundamental challenges in machine
learning. Previously, a UQ method called DISCO Nets has been proposed
(Bouchacourt et al., 2016), which trains a neural network by minimizing the
energy score. In this method, a random noise vector in
$\mathbb{R}^{10\text{--}100}$ is concatenated with the original input vector in
order to produce a diverse ensemble forecast despite using a single neural
network. While this method has shown promising performance on a hand pose
estimation task in computer vision, it remained unexplored whether this method
works as nicely for regression on tabular data, and how it competes with more
recent advanced UQ methods such as NGBoost. In this paper, we propose an
improved neural architecture of DISCO Nets that admits faster and more stable
training while only using a compact noise vector of dimension $\sim
\mathcal{O}(1)$. We benchmark this approach on miscellaneous real-world tabular
datasets and confirm that it is competitive with or even superior to standard
UQ baselines. Moreover we observe that it exhibits better point forecast
performance than a neural network of the same size trained with the
conventional mean squared error. As another advantage of the proposed method,
we show that local feature importance computation methods such as SHAP can be
easily applied to any subregion of the predictive distribution. A new
elementary proof for the validity of using the energy score to learn predictive
distributions is also provided.
