论文标题
持续学习和私人学习
Continual Learning and Private Unlearning
论文作者
论文摘要
随着智能代理人在更长的时间内变得自主，他们最终可能会成为特定人的终身对手。如果是这样，用户可能希望代理商暂时掌握任务，但后来由于隐私问题而忘记了任务。但是，使代理到\ emph {私人}用户在不降低其余知识的情况下指定的内容是一个具有挑战性的问题。为了应对这一挑战，本文正式将这种持续的学习和私人学习（CLPU）问题形式化。该论文进一步引入了一个直接但完全私人的解决方案Clpu-der ++，作为解决CLPU问题的第一步，以及一系列精心设计的基准问题，以评估所提出的解决方案的有效性。该代码可在https://github.com/cranial-xix/continual-learning-private-unlearning上找到。
As intelligent agents become autonomous over longer periods of time, they may eventually become lifelong counterparts to specific people. If so, it may be common for a user to want the agent to master a task temporarily but later on to forget the task due to privacy concerns. However enabling an agent to \emph{forget privately} what the user specified without degrading the rest of the learned knowledge is a challenging problem. With the aim of addressing this challenge, this paper formalizes this continual learning and private unlearning (CLPU) problem. The paper further introduces a straightforward but exactly private solution, CLPU-DER++, as the first step towards solving the CLPU problem, along with a set of carefully designed benchmark problems to evaluate the effectiveness of the proposed solution. The code is available at https://github.com/Cranial-XIX/Continual-Learning-Private-Unlearning.
