Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
Researchers have developed a novel method called Richardson-SGD to address gradient bias in stochastic gradient descent when dealing with incomplete data. The technique involves deliberately introducing additional missingness to data, then combining gradients from different levels of missingness to cancel out bias. This approach is model-agnostic, computationally efficient, and has shown empirical improvements in optimization and estimation for various models, even when combined with existing imputation methods like MICE. AI
IMPACT Introduces a novel technique to improve the accuracy of machine learning models trained on incomplete datasets.