A duality framework for analyzing random feature and two-layer neural networks
分析随机特征与双层神经网络的对偶框架
We consider the problem of learning functions within the Fp,π and Barron spaces, which play crucial roles in understanding random feature models (RFMs), two-layer neural networks as well as kernel methods. Leveraging tools from information-based complexity (IBC), we establish a dual equivalence between approximation and estimation and then apply it to study the learning of the preceding function spaces. The duality allows us to focus on the more tractable problem between…