Matrix Sensing with Kernel Optimal Loss: Robustness and Optimization Landscape

Abstract

We study how loss-function choice affects robustness and optimization landscapes in noisy matrix sensing. A kernel-based residual loss matches mean-squared error under Gaussian noise while remaining stable for heavy-tailed and heterogeneous corruption. Theoretical and empirical results show improved robustness and convergence across diverse noise distributions.