198 citations · 379 across the 15 of their papers we have counts for
3 papers · 1 filter
ATOMO: Communication-efficient Learning via Atomic Sparsification
Hongyi Wang, Scott Sievert, Zachary Charles +3
Distributed model training suffers from communication overheads due to frequent gradient updates transmitted between compute nodes. To mitigate these overheads, several studies pro…
Online Learning for Changing Environments using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright +1
A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any onlin…
Improved Strongly Adaptive Online Learning using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Rebecca Willett +1
This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a st…