no code implementations • 16 Feb 2024 • Jaewook Lee, Hanseul Cho, Chulhee Yun
The Gradient Descent-Ascent (GDA) algorithm, designed to solve minimax optimization problems, takes the descent and ascent steps either simultaneously (Sim-GDA) or alternately (Alt-GDA).
1 code implementation • NeurIPS 2023 • Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun
Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditional on the sensitive attributes, match one another.
no code implementations • 12 Oct 2022 • Hanseul Cho, Chulhee Yun
Stochastic gradient descent-ascent (SGDA) is one of the main workhorses for solving finite-sum minimax optimization problems.
