论文标题
改进的双层模型：具有理论保证的快速和最佳算法
Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee
论文作者
论文摘要
由于许多机器学习问题的层次结构，二重性编程越来越重要，但是，内部和外部问题之间的复杂相关性使得解决极具挑战性。尽管已经提出了基于自动分化的几种直观算法并在某些应用中取得了成功，但并没有对找到二重性模型的最佳表述受到很多关注。是否存在更好的配方仍然是一个空旷的问题。在本文中，我们提出了一个改进的双层模型，该模型与当前配方相比更快，更好。我们在两个任务上提供理论保证和评估结果：数据超清洁和超表示学习。经验结果表明，我们的模型的表现优于当前的双重模型。 \ emph {这是与\ citet {liu2020 generic}的并发作品，我们提交给ICML 2020。现在，我们将其放在arxiv上以获取记录。}
Due to the hierarchical structure of many machine learning problems, bilevel programming is becoming more and more important recently, however, the complicated correlation between the inner and outer problem makes it extremely challenging to solve. Although several intuitive algorithms based on the automatic differentiation have been proposed and obtained success in some applications, not much attention has been paid to finding the optimal formulation of the bilevel model. Whether there exists a better formulation is still an open problem. In this paper, we propose an improved bilevel model which converges faster and better compared to the current formulation. We provide theoretical guarantee and evaluation results over two tasks: Data Hyper-Cleaning and Hyper Representation Learning. The empirical results show that our model outperforms the current bilevel model with a great margin. \emph{This is a concurrent work with \citet{liu2020generic} and we submitted to ICML 2020. Now we put it on the arxiv for record.}
