论文标题
不确定性估计的合奏：先前功能和自举的好处
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
论文作者
论文摘要
在机器学习中，代理需要估计不确定性，以有效地探索和适应并做出有效的决定。不确定性估计的一种常见方法保持模型的整体。近年来，已经提出了几种用于培训合奏的方法，并且在这些方法的各种成分的重要性方面占上风。在本文中，我们旨在解决已受到质疑的两种成分的好处 - 先前的功能和引导。我们表明，先前的函数可以显着改善整体代理在输入之间的关节预测，如果信噪比在输入之间变化，则自举效率可以带来额外的好处。我们的主张是通过理论和实验结果证明的。
In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains an ensemble of models. In recent years, several approaches have been proposed for training ensembles, and conflicting views prevail with regards to the importance of various ingredients of these approaches. In this paper, we aim to address the benefits of two ingredients -- prior functions and bootstrapping -- which have come into question. We show that prior functions can significantly improve an ensemble agent's joint predictions across inputs and that bootstrapping affords additional benefits if the signal-to-noise ratio varies across inputs. Our claims are justified by both theoretical and experimental results.
