Robust Gradient Descent for Phase Retrieval
This work addresses a critical gap in robust statistical learning for non-convex problems like phase retrieval, offering incremental improvements for applications in signal processing and imaging under adversarial conditions.
The paper tackles the non-convex phase retrieval problem under heavy-tailed noise and adversarial corruption by enhancing the Wirtinger Flow algorithm with robust gradient descent techniques, achieving recovery in scenarios with fourth moment bounded noise and unknown noise distributions.
Recent progress in robust statistical learning has mainly tackled convex problems, like mean estimation or linear regression, with non-convex challenges receiving less attention. Phase retrieval exemplifies such a non-convex problem, requiring the recovery of a signal from only the magnitudes of its linear measurements, without phase (sign) information. While several non-convex methods, especially those involving the Wirtinger Flow algorithm, have been proposed for noiseless or mild noise settings, developing solutions for heavy-tailed noise and adversarial corruption remains an open challenge. In this paper, we investigate an approach that leverages robust gradient descent techniques to improve the Wirtinger Flow algorithm's ability to simultaneously cope with fourth moment bounded noise and adversarial contamination in both the inputs (covariates) and outputs (responses). We address two scenarios: known zero-mean noise and completely unknown noise. For the latter, we propose a preprocessing step that alters the problem into a new format that does not fit traditional phase retrieval approaches but can still be resolved with a tailored version of the algorithm for the zero-mean noise context.