论文标题
合奏多品质：不确定性定量的自适应灵活分布预测
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
论文作者
论文摘要
我们提出了一种新颖，简洁且有效的方法，用于分布预测，以量化机器学习中的不确定性。它在回归任务中包含了$ \ mathbb {p} $ \ mathbb {p}（\ mathbf {y} | \ mathbf {y} | \ mathbf {y} = x）$的自适应灵活分布预测。该条件分布的概率级别分数传播了间隔$（0,1）$，这是由具有直觉和解释性设计的加性模型增强的。我们在结构完整性和$ \ mathbb {p}（\ Mathbf {y} | \ Mathbf {X} = X）$之间寻求适应性平衡，而高斯假设导致缺乏灵活性的真实数据和高度灵活的方法（例如，在没有分布结构的情况下估计量化的量化），并且可以分布良好的结构。我们提出的这种称为EMQ的合奏多品质方法完全由数据驱动，并且可以逐渐离开高斯，并发现增强中的最佳条件分布。在UCI数据集的广泛回归任务中，我们表明EMQ与许多最近的不确定性量化方法相比，EMQ达到了最先进的性能。 Visualization results further illustrate the necessity and the merits of such an ensemble model.
We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$ in regression tasks. This conditional distribution's quantiles of probability levels spreading the interval $(0,1)$ are boosted by additive models which are designed by us with intuitions and interpretability. We seek an adaptive balance between the structural integrity and the flexibility for $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$, while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e.g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization. This ensemble multi-quantiles approach called EMQ proposed by us is totally data-driven, and can gradually depart from Gaussian and discover the optimal conditional distribution in the boosting. On extensive regression tasks from UCI datasets, we show that EMQ achieves state-of-the-art performance comparing to many recent uncertainty quantification methods. Visualization results further illustrate the necessity and the merits of such an ensemble model.
