Traditional regression and prediction tasks often only provide deterministic
point estimates. To estimate the uncertainty or distribution information of the
response variable, methods such as Bayesian inference, model ensembling, or MC
Dropout are typically used. These methods either assume that the posterior
distribution of samples follows a Gaussian process or require thousands of
forward passes for sample generation. We propose a novel approach called
DistPred for regression and forecasting tasks, which overcomes the limitations
of existing methods while remaining simple and powerful. Specifically, we
transform proper scoring rules that measure the discrepancy between the
predicted distribution and the target distribution into a differentiable
discrete form and use it as a loss function to train the model end-to-end. This
allows the model to sample numerous samples in a single forward pass to
estimate the potential distribution of the response variable. We have compared
our method with several existing approaches on multiple datasets and achieved
state-of-the-art performance. Additionally, our method significantly improves
computational efficiency. For example, compared to state-of-the-art models,
DistPred has a 90x faster inference speed. Experimental results can be
reproduced through this https URL

DistPred 是一种用于回归和预测任务的新方法，通过使用可微分的离散形式的得分规则，可以在单次前向传递中生成大量样本来估计响应变量的潜在分布，并具有比现有方法更简单且更强大的性能和 90 倍的推理速度。