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. Code will soon be available at \url{https://github.com/Wang-ML-Lab/variational-imbalanced-regression}.

翻译：现有回归模型在标签分布非平衡的情况下，通常在准确性和不确定性估计两方面均表现不佳。本文提出一种概率深度学习模型——变分非平衡回归（VIR），该模型不仅在非平衡回归中表现优异，还能自然产生合理的不确定性估计。与假设表示独立同分布（即一个数据点的表示不受其他数据点直接影响）的典型变分自编码器不同，我们的VIR通过借用具有相似回归标签的数据来计算潜在表示的变分分布；此外，与产生点估计的确定性回归模型不同，VIR预测完整的正态逆伽马分布，并通过调制相关的共轭分布对非平衡数据施加概率重加权，从而提供更优的不确定性估计。在多个真实数据集上的实验表明，我们的VIR在准确性和不确定性估计两方面均能超越最先进的非平衡回归模型。代码即将在\url{https://github.com/Wang-ML-Lab/variational-imbalanced-regression}发布。