Existing regression models tend to fall short in both accuracy and
uncertainty estimation when the label distribution is imbalanced. In this
paper, we propose a probabilistic deep learning model, dubbed variational
imbalanced regression (VIR), which not only performs well in imbalanced
regression but naturally produces reasonable uncertainty estimation as a
byproduct. Different from typical variational autoencoders assuming I.I.D.
representations (a data point's representation is not directly affected by
other data points), our VIR borrows data with similar regression labels to
compute the latent representation's variational distribution; furthermore,
different from deterministic regression models producing point estimates, VIR
predicts the entire normal-inverse-gamma distributions and modulates the
associated conjugate distributions to impose probabilistic reweighting on the
imbalanced data, thereby providing better uncertainty estimation. Experiments
in several real-world datasets show that our VIR can outperform
state-of-the-art imbalanced regression models in terms of both accuracy and
uncertainty estimation.

本文介绍了一种概率深度学习模型，名为 Variational Imbalanced Regression (VIR)。该模型可以在标记分布不平衡的情况下，准确地进行回归，并自然地产生合理的不确定度估计，实验证明我们的 VIR 在准确性和不确定度估计方面均可优于现有的不平衡回归模型。