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cs.LG2024
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
Yinghui Huang, Zihao Tang, Xiangyu Chang
The biases and discrimination of machine learning algorithms have attracted significant attention, leading to the development of various algorithms tailored to specific contexts. H…
cs.LG2024
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
Hao Di, Haishan Ye, Yueling Zhang +3
Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods.…
math.OC2024
Anderson Acceleration Without Restart: A Novel Method with -Step Super Quadratic Convergence Rate
Haishan Ye, Dachao Lin, Xiangyu Chang +1
In this paper, we propose a novel Anderson's acceleration method to solve nonlinear equations, which does \emph{not} require a restart strategy to achieve numerical stability. We p…