2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2022
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Zhishuai Guo, Rong Jin, Jiebo Luo +1
In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective…
math.OC2021★ 1 cited
Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator
Zhishuai Guo, Yi Xu, Wotao Yin +2
Although adaptive optimization algorithms have been successful in many applications, there are still some mysteries in terms of convergence analysis that have not been unraveled. T…
cs.LG2020★ 2 cited
Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Rong Jin +2
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple mod…