all AI news
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits. (arXiv:2202.08302v2 [cs.IT] UPDATED)
June 29, 2022, 1:11 a.m. | Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz
stat.ML updates on arXiv.org arxiv.org
We consider the distributed SGD problem, where a main node distributes
gradient calculations among $n$ workers. By assigning tasks to all the workers
and waiting only for the $k$ fastest ones, the main node can trade-off the
algorithm's error with its runtime by gradually increasing $k$ as the algorithm
evolves. However, this strategy, referred to as adaptive $k$-sync, neglects the
cost of unused computations and of communicating models to workers that reveal
a straggling behavior. We propose a cost-efficient scheme …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Staff Software Engineer, Generative AI, Google Cloud AI
@ Google | Mountain View, CA, USA; Sunnyvale, CA, USA
Expert Data Sciences
@ Gainwell Technologies | Any city, CO, US, 99999