9 citations · 35 across the 8 of their papers we have counts for
4 papers · 1 filter
Estimating and Explaining Model Performance When Both Covariates and Labels Shift
Lingjiao Chen, Matei Zaharia, James Zou
Deployed machine learning (ML) models often encounter new user data that differs from their training data. Therefore, estimating how well a given model might perform on the new dat…
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…
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…
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…