32 citations · 64 across the 26 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 4 cited
MixML: A Unified Analysis of Weakly Consistent Parallel Learning
Yucheng Lu, Jack Nash, Christopher De Sa
Parallelism is a ubiquitous method for accelerating machine learning algorithms. However, theoretical analysis of parallel learning is usually done in an algorithm- and protocol-sp…
cs.LG2020★ 2 cited
Optimal Complexity in Decentralized Training
Yucheng Lu, Christopher De Sa
Decentralization is a promising method of scaling up parallel machine learning systems. In this paper, we provide a tight lower bound on the iteration complexity for such methods i…
cs.LG2020★ 32 cited
Moniqua: Modulo Quantized Communication in Decentralized SGD
Yucheng Lu, Christopher De Sa
Running Stochastic Gradient Descent (SGD) in a decentralized fashion has shown promising results. In this paper we propose Moniqua, a technique that allows decentralized SGD to use…