论文标题
学习要记住什么
Learning what to remember
论文作者
论文摘要
我们考虑了一种终生的学习情况，其中学习者面临着永无止境和任意的事实流，并且必须决定在有限的记忆中保留哪些事实。我们介绍了基于在线学习框架的数学模型，在线学习框架中，学习者对自己也受到内存约束的专家集合进行衡量，并反映了要记住的内容的不同政策。散布在事实流中是偶尔的问题，如果这些问题不记得相应的事实，那么学习者就会造成损失。它的目标是几乎与事后观察的最佳专家一样，同时使用大致相同的内存。我们确定了在此内存受限的方案中使用多重权重更新算法的困难，并设计了一种替代方案，其遗憾的保证接近最佳。
We consider a lifelong learning scenario in which a learner faces a neverending and arbitrary stream of facts and has to decide which ones to retain in its limited memory. We introduce a mathematical model based on the online learning framework, in which the learner measures itself against a collection of experts that are also memory-constrained and that reflect different policies for what to remember. Interspersed with the stream of facts are occasional questions, and on each of these the learner incurs a loss if it has not remembered the corresponding fact. Its goal is to do almost as well as the best expert in hindsight, while using roughly the same amount of memory. We identify difficulties with using the multiplicative weights update algorithm in this memory-constrained scenario, and design an alternative scheme whose regret guarantees are close to the best possible.
