37 citations · 56 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
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…
Optimally Compressed Nonparametric Online Learning
Alec Koppel, Amrit Singh Bedi, Ketan Rajawat +1
Batch training of machine learning models based on neural networks is now well established, whereas to date streaming methods are largely based on linear models. To go beyond linea…