Abstract: Traditional adaptive beamformers often exhibit performance degradation under model mismatches or limited sample scenarios. To address these limitations, this paper proposes a robust adaptive ...

This repository contains the core recommendation system powering the "For You" feed on X. It combines in-network content (from accounts you follow) with out-of-network content (discovered through ...

Abstract: Factorizing a low-rank matrix into two matrix factors with low dimensions from its noisy observations is a classical but challenging problem arising from real-world applications. This paper ...