Learning Orthogonal Multi-Index Models: A Fine-Grained Information Exponent Analysis
Yunwei Ren, Jason Lee
The information exponent (Ben Arous et al. [2021]) and its extensions --- which are equivalent to the lowest   degree in the Hermite expansion of the link function (after a potential label transform) for Gaussian single-index   models --- have played an important role in predicting the sample complexity of online stochastic gradient descent   (SGD) in various learning tasks. In this work, we demonstrate that, for multi-index models, focusing solely on the   lowest degree can miss key structural details of the model and result in suboptimal rates.   Specifically, we consider the task of learning target functions of form $f_*(x) = \sum_{k=1}^{P} \phi(v_k^* \cdot x)$,   where $P \le d$, the ground-truth directions $\\{ v_k^* \\}_{k=1}^P$ are orthonormal, and the information exponent of   $\phi$ is $L$. Based on the theory of information exponent, when $L = 2$, only the relevant subspace (not the exact   directions) can be recovered due to the rotational invariance of the second-order terms, and when $L > 2$,   recovering the directions using online SGD require $\tilde{O}(P d^{L-1})$ samples. In this work, we show that by   considering both second- and higher-order terms, we can first learn the relevant space using the second-order  terms, and then the exact directions using the higher-order terms, and the overall sample and complexity of online   SGD is $\tilde{O}( P d P^{L-2} )$.