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cs.LG2022
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents
Abdullah Basar Akbay, Cihan Tepedelenlioglu
This study considers a federated learning setup where cost-sensitive and strategic agents train a learning model with a server. During each round, each agent samples a minibatch of…
cs.LG2018
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization
Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan +3
Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical exampl…