论文标题
痕迹规范正规化用于多任务学习的数据稀缺数据
Trace norm regularization for multi-task learning with scarce data
论文作者
论文摘要
多任务学习利用了多个任务之间的结构相似性，尽管样本很少。由应用于数据筛选任务的神经网络的最新成功的激励，我们考虑了线性低维共享表示模型。尽管有广泛的文献，但现有的理论结果要么保证估计率较弱，要么每个任务都需要大量样本。当每个任务的样本数量较小时，这项工作提供了针对跟踪规范正规化估计器的第一个估计误差。学习数据筛选任务的痕量规范正规化的优点扩展到元学习，并在合成数据集上得到证实。
Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples. Motivated by the recent success of neural networks applied to data-scarce tasks, we consider a linear low-dimensional shared representation model. Despite an extensive literature, existing theoretical results either guarantee weak estimation rates or require a large number of samples per task. This work provides the first estimation error bound for the trace norm regularized estimator when the number of samples per task is small. The advantages of trace norm regularization for learning data-scarce tasks extend to meta-learning and are confirmed empirically on synthetic datasets.
