Bayesian Smoothing and Variable Selection Using Variational Automatic Relevance Determination
提出变分自动相关性确定方法，用于高维稀疏加性回归模型，能独立评估每个特征的平滑性并判断其贡献是零、线性还是非线性，并给出高效坐标下降算法。
We introduce Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smoothness of each feature while enabling precise determination of whether a feature’s contribution to the response is zero, linear, or nonlinear. Further, an efficient coordinate descent algorithm is introduced to implement VARD. Empirical evaluations on simulated and real-world data underscore VARD’s superiority over alternative variable selection methods for additive models.