论文标题
阈值自适应验证：调整图形套索以进行图形恢复
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
论文作者
论文摘要
许多机器学习算法都是正规化优化问题，但它们的性能取决于需要对每个应用程序进行校准的正规化参数。在本文中，我们提出了一种常规校准方案，用于正则优化问题，并将其应用于图形套索，这是高斯图形建模的一种方法。该方案配备了理论保证，并激发了可以改善图形恢复的阈值管道。此外，最多需要一行在正规化路径上进行搜索，校准方案在计算上比基于重新采样的竞争方案更有效。最后，我们在模拟中表明，我们的方法可以大大改善其他方法的图形恢复。
Many Machine Learning algorithms are formulated as regularized optimization problems, but their performance hinges on a regularization parameter that needs to be calibrated to each application at hand. In this paper, we propose a general calibration scheme for regularized optimization problems and apply it to the graphical lasso, which is a method for Gaussian graphical modeling. The scheme is equipped with theoretical guarantees and motivates a thresholding pipeline that can improve graph recovery. Moreover, requiring at most one line search over the regularization path, the calibration scheme is computationally more efficient than competing schemes that are based on resampling. Finally, we show in simulations that our approach can improve on the graph recovery of other approaches considerably.
