518 citations · 1.3k across the 51 of their papers we have counts for
6 papers · 1 filter
Deep Learning with Gaussian Differential Privacy
Zhiqi Bu, Jinshuo Dong, Qi Long +1
Deep learning models are often trained on datasets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increas…
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
Matteo Sordello, Niccolò Dalmasso, Hangfeng He +1
This paper proposes SplitSGD, a new dynamic learning rate schedule for stochastic optimization. This method decreases the learning rate for better adaptation to the local geometry…
The Local Elasticity of Neural Networks
Hangfeng He, Weijie J. Su
This paper presents a phenomenon in neural networks that we refer to as \textit{local elasticity}. Roughly speaking, a classifier is said to be locally elastic if its prediction at…
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Zhiqi Bu, Jason Klusowski, Cynthia Rush +1
SLOPE is a relatively new convex optimization procedure for high-dimensional linear regression via the sorted l1 penalty: the larger the rank of the fitted coefficient, the larger…
Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
Qingyang Wu, He Li, Lexin Li +1
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep…
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
Bin Shi, Simon S. Du, Weijie J. Su +1
We study first-order optimization methods obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov's accelerated gradient methods (NAGs) and Polyak…