AI RESEARCH
Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
arXiv CS.LG
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ArXi:2604.21203v1 Announce Type: cross We study online inference and asymptotic covariance estimation for the stochastic gradient descent (SGD) algorithm. While classical methods (such as plug-in and batch-means estimators) are available, they either require inaccessible second-order (Hessian) information or suffer from slow convergence. To address these challenges, we propose a novel, fully online de-biased covariance estimator that eliminates the need for second-order derivatives while significantly improving estimation accuracy.