论文标题
线性回归模型的在线遗忘过程
Online Forgetting Process for Linear Regression Models
论文作者
论文摘要
在欧盟的“被遗忘的权利”法规中，我们启动了对统计数据删除问题的研究，其中仅在有限的时间内访问用户数据。该设置用\ textit {常数内存限制}将其作为在线监督学习任务。我们为低维情况提出了一种缺失感知算法\ texttt {fifd-ols}，并且由于数据删除操作而导致灾难性的等级摆动现象，这导致统计效率低下。作为一种补救措施，我们提出\ texttt {fifd-aptaptive ridge}算法，并具有新颖的在线正规化方案，从而有效地抵消了删除的不确定性。从理论上讲，我们为在线忘记算法提供了累积的遗憾上限。在实验中，我们显示了\ texttt {fifd-appaptive ridge}的表现优于固定正则化水平的脊回归算法，并希望对更复杂的统计模型有所了解。
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time. This setting is formulated as an online supervised learning task with \textit{constant memory limit}. We propose a deletion-aware algorithm \texttt{FIFD-OLS} for the low dimensional case, and witness a catastrophic rank swinging phenomenon due to the data deletion operation, which leads to statistical inefficiency. As a remedy, we propose the \texttt{FIFD-Adaptive Ridge} algorithm with a novel online regularization scheme, that effectively offsets the uncertainty from deletion. In theory, we provide the cumulative regret upper bound for both online forgetting algorithms. In the experiment, we showed \texttt{FIFD-Adaptive Ridge} outperforms the ridge regression algorithm with fixed regularization level, and hopefully sheds some light on more complex statistical models.
