120 citations · 154 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
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…
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Jiaxing Zhang, Hoang Tran, Guannan Zhang
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two limi…