论文标题
通过利用示例难度，更紧密的pac-bayes泛化范围
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty
论文作者
论文摘要
我们引入了多余风险的修改版本，该版本可用于获得更严格，快速的Pac-bayesian泛化边界。这种修改的多余风险利用了有关数据示例相对硬度的信息，以减少其经验的差异，从而收紧界限。我们将其与$ [-1，1] $的新结合结合在一起，值得$ [ - 1，1] $（可能是非独立的）签名损失，当它们的经验上的差异较低左右$ 0 $时，这会更加有利。主要的新技术工具是相互依存的随机向量序列的新结果，这可能具有独立感兴趣。我们在许多现实世界数据集上经验评估了这些新界限。
We introduce a modified version of the excess risk, which can be used to obtain tighter, fast-rate PAC-Bayesian generalisation bounds. This modified excess risk leverages information about the relative hardness of data examples to reduce the variance of its empirical counterpart, tightening the bound. We combine this with a new bound for $[-1, 1]$-valued (and potentially non-independent) signed losses, which is more favourable when they empirically have low variance around $0$. The primary new technical tool is a novel result for sequences of interdependent random vectors which may be of independent interest. We empirically evaluate these new bounds on a number of real-world datasets.
