73 citations · 170 across the 18 of their papers we have counts for
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cs.LG2021★ 2 cited
Adaptive Differentially Private Empirical Risk Minimization
Xiaoxia Wu, Lingxiao Wang, Irina Cristali +2
We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization. At each iteration, the random noise added to the gradient i…
stat.ML2021
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Xiaoxia Wu, Yuege Xie, Simon Du +1
We propose a computationally-friendly adaptive learning rate schedule, "AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descen…
cs.CL2021★ 2 cited
LEAP: Learnable Pruning for Transformer-based Models
Zhewei Yao, Xiaoxia Wu, Linjian Ma +4
Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. However, current pruning algorithms e…