Preprint
Jul 2026
Optimization under Persistent State-Dependent Bias: Gradient-based Method and Complexity Analysis
This paper studies the convergence of stochastic gradient descent when the implemented updates are subject to a persistent and state-dependent bias, in which the desired update is scaled by response functions component-wise, and proposes a gradient-based algorithm, termed Residual Learning.
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen et al.
· 0 citations