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eess.SP2020
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems
Zhan Gao, Alec Koppel, Alejandro Ribeiro
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we s…
eess.SP2020
Collaborative Beamforming Under Localization Errors: A Discrete Optimization Approach
Erfaun Noorani, Yagiz Savas, Alec Koppel +3
We consider a network of agents that locate themselves in an environment through sensor measurements and aim to transmit a message signal to a base station via collaborative beamfo…