Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random

no code implementations • 24 May 2024 • Mingming Ha, Xuewen Tao, Wenfang Lin, Qionxu Ma, Wujiang Xu, Linxun Chen

In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction performance of models.

Generalization Bounds Missing Values +1