7 citations · 9 across the 6 of their papers we have counts for
9 papers
Differentially Private Online-to-Batch for Smooth Losses
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
We develop a new reduction that converts any online convex optimization algorithm suffering regret into an -differentially private stochastic convex optimization a…
Momentum Aggregation for Private Non-convex ERM
Hoang Tran, Ashok Cutkosky
We introduce new algorithms and convergence guarantees for privacy-preserving non-convex Empirical Risk Minimization (ERM) on smooth -dimensional objectives. We develop an impro…
Compressive-sensing-assisted mixed integer optimization for dynamical system discovery with highly noisy data
Zhongshun Shi, Hang Ma, Hoang Tran +1
The identification of governing equations for dynamical systems is everlasting challenges for the fundamental research in science and engineering. Machine learning has exhibited gr…
Better SGD using Second-order Momentum
Hoang Tran, Ashok Cutkosky
We develop a new algorithm for non-convex stochastic optimization that finds an -critical point in the optimal stochastic gradient and Hessian-vector product computa…
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
Hoang Tran, Guannan Zhang
The local gradient points to the direction of the steepest slope in an infinitesimal neighborhood. An optimizer guided by the local gradient is often trapped in local optima when t…
Analysis of The Ratio of and Norms in Compressed Sensing
Yiming Xu, Akil Narayan, Hoang Tran +1
We first propose a novel criterion that guarantees that an -sparse signal is the local minimizer of the objective; our criterion is interpretable and useful in p…