1.5k citations · 1.6k across the 23 of their papers we have counts for
Showing 2022Show all
2 papers · 1 filter
cs.DS2022
Discrepancy Minimization via Regularization
Lucas Pesenti, Adrian Vladu
We introduce a new algorithmic framework for discrepancy minimization based on regularization. We demonstrate how varying the regularizer allows us to re-interpret several breakthr…
cs.LG2022★ 1 cited
CrAM: A Compression-Aware Minimizer
Alexandra Peste, Adrian Vladu, Eldar Kurtic +2
Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compressi…