It is desirable to have accurate uncertainty estimation from a single deterministic forward-pass model, as traditional methods for uncertainty quantification are computationally expensive. However, this is difficult because single forward-pass models do not sample weights during inference and often make assumptions about the target distribution, such as assuming it is Gaussian. This can be restrictive in regression tasks, where the mean and standard deviation are inadequate to model the target distribution accurately. This paper proposes a deep Bayesian quantile regression model that can estimate the quantiles of a continuous target distribution without the Gaussian assumption. The proposed method is based on evidential learning, which allows the model to capture aleatoric and epistemic uncertainty with a single deterministic forward-pass model. This makes the method efficient and scalable to large models and datasets. We demonstrate that the proposed method achieves calibrated uncertainties on non-Gaussian distributions, disentanglement of aleatoric and epistemic uncertainty, and robustness to out-of-distribution samples.

翻译：传统的概率不确定性量化方法计算成本高昂，因此希望从单一确定性前馈模型中获得准确的不确定性估计。然而，这极具挑战性，因为单一前馈模型在推理过程中不进行权重采样，且通常对目标分布作出假设（如假设服从高斯分布）。这在回归任务中具有局限性，因为仅依赖均值与标准差难以准确建模目标分布。本文提出一种无需高斯假设即可估计连续目标分布分位数的深度贝叶斯分位数回归模型。该方法基于证据学习理论，能够通过单一确定性前馈模型同时捕获偶然不确定性与认知不确定性，从而保证方法的高效性及对大规模模型与数据集的可扩展性。实验表明，该方法可在非高斯分布上实现校准的不确定性估计，有效分离偶然与认知不确定性，并具备对分布外样本的鲁棒性。