1 citations · 2 across the 14 of their papers we have counts for
5 papers · 1 filter
Continuum-armed Bandit Optimization with Batch Pairwise Comparison Oracles
Xiangyu Chang, Xi Chen, Yining Wang +1
This paper studies a bandit optimization problem where the goal is to maximize a function over periods for some unknown strongly concave function . We consider a new…
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
Haishan Ye, Yinghui Huang, Hao Di +1
We propose an enhanced zeroth-order stochastic Frank-Wolfe framework to address constrained finite-sum optimization problems, a structure prevalent in large-scale machine-learning…
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…
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.…
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Hao Di, Yi Yang, Haishan Ye +1
Personalization aims to characterize individual preferences and is widely applied across many fields. However, conventional personalized methods operate in a centralized manner, po…