Sparse Target

Sparse target problems, focusing on scenarios with limited or incomplete data, are a growing area of research in machine learning and signal processing. Current work investigates how model architectures, such as random feature models and transformers, perform under these conditions, exploring the impact of factors like data representation and initialization schemes on learning dynamics and generalization. A key focus is developing robust and efficient algorithms that can accurately estimate parameters or perform tasks even with limited information, improving upon existing methods that suffer from suboptimal error rates. These advancements have significant implications for various applications, including robust statistics, network analysis, and multi-object tracking, where data sparsity is a common challenge.