56 citations · 283 across the 30 of their papers we have counts for
4 papers · 1 filter
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Bao Wang, Difan Zou, Quanquan Gu +1
As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior samplin…
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning
Bao Wang, Stanley J. Osher
Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning research. In this paper, we replace the…
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo +2
Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones. To mitigate this degradation,…
A Deterministic Gradient-Based Approach to Avoid Saddle Points
Lisa Maria Kreusser, Stanley J. Osher, Bao Wang
Loss functions with a large number of saddle points are one of the major obstacles for training modern machine learning models efficiently. First-order methods such as gradient des…