8 citations · 9 across the 13 of their papers we have counts for
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cs.LG2023
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Sudeep Salgia, Sattar Vakili, Qing Zhao
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random…
cs.LG2023★ 8 cited
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
Dan Ben Ami, Kobi Cohen, Qing Zhao
Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchang…
cs.LG2023
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
Sudeep Salgia, Qing Zhao, Tamir Gabay +1
We consider the problem of online stochastic optimization in a distributed setting with clients connected through a central server. We develop a distributed online learning alg…