Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise

Abstract

We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goal is to find a "best-fit" function. More precisely, given training samples from a reference distribution $p_0$, the goal is to approximate the vector $\mathbf{w}^*$ which minimizes the squared loss with respect to the worst-case distribution that is close in $\chi^2$-divergence to $p_{0}$. We design a computationally efficient algorithm that recovers a vector $ \hat{\mathbf{w}}$ satisfying $\mathbb{E}\_{p^*} (\sigma(\hat{\mathbf{w}} \cdot \mathbf{x}) - y)^2 \leq C \hspace{0.2em} \mathbb{E}\_{p^*} (\sigma(\mathbf{w}^* \cdot \mathbf{x}) - y)^2 + \epsilon$, where $C>1$ is a dimension-independent constant and $(\mathbf{w}^*, p^*)$ is the witness attaining the min-max risk $\min_{\mathbf{w}:\|\mathbf{w}\| \leq W} \max\_{p} \mathbb{E}\_{(\mathbf{x}, y) \sim p} (\sigma(\mathbf{w} \cdot \mathbf{x}) - y)^2 - \nu \chi^2(p, p_0)$. Our algorithm follows the primal-dual framework and is designed by directly bounding the risk with respect to the original, nonconvex $L_2^2$ loss. From an optimization standpoint, our work opens new avenues for the design of primal-dual algorithms under structured nonconvexity.