9 citations · 18 across the 3 of their papers we have counts for
6 papers
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Leshang Chen, Hongyi Wang +2
There has been a growing need to provide Byzantine-resilience in distributed model training. Existing robust distributed learning algorithms focus on developing sophisticated robus…
Did the Model Change? Efficiently Assessing Machine Learning API Shifts
Lingjiao Chen, Tracy Cai, Matei Zaharia +1
Machine learning (ML) prediction APIs are increasingly widely used. An ML API can change over time due to model updates or retraining. This presents a key challenge in the usage of…
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply
Lingjiao Chen, Matei Zaharia, James Zou
Prediction APIs offered for a fee are a fast-growing industry and an important part of machine learning as a service. While many such services are available, the heterogeneity in t…
The Effect of Network Width on the Performance of Large-batch Training
Lingjiao Chen, Hongyi Wang, Jinman Zhao +2
Distributed implementations of mini-batch stochastic gradient descent (SGD) suffer from communication overheads, attributed to the high frequency of gradient updates inherent in sm…
Model-based Pricing for Machine Learning in a Data Marketplace
Lingjiao Chen, Paraschos Koutris, Arun Kumar
Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the t…
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles +1
Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a…