4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2025
DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling
Yubo Gao, Renbo Tu, Gennady Pekhimenko +1
Differentially-Private SGD (DP-SGD) and its adaptive variant DP-Adam are powerful techniques to protect user privacy when using sensitive data to train neural networks. During trai…
cs.LG2021★ 4 cited
A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
Geoffrey X. Yu, Yubo Gao, Pavel Golikov +1
Deep learning researchers and practitioners usually leverage GPUs to help train their deep neural networks (DNNs) faster. However, choosing which GPU to use is challenging both bec…