7 papers · 1 filter
Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu +1
We consider the problem of differentially private (DP) convex empirical risk minimization (ERM). While the standard DP-SGD algorithm is theoretically well-established, practical im…
Natural Policy Gradient for Average Reward Non-Stationary RL
Neharika Jali, Eshika Pathak, Pranay Sharma +2
We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rew…
Federated Communication-Efficient Multi-Objective Optimization
Baris Askin, Pranay Sharma, Gauri Joshi +1
We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions. MOO has been extensively studied in th…
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
Aleksandar Armacki, Shuhua Yu, Pranay Sharma +4
We study high-probability convergence in online learning, in the presence of heavy-tailed noise. To combat the heavy tails, a general framework of nonlinear SGD methods is consider…
Optimized Tradeoffs for Private Prediction with Majority Ensembling
Shuli Jiang, Qiuyi, Zhang +1
We study a classical problem in private prediction, the problem of computing an -differentially private majority of -differentially private algorithms for…
FedAST: Federated Asynchronous Simultaneous Training
Baris Askin, Pranay Sharma, Carlee Joe-Wong +1
Federated Learning (FL) enables edge devices or clients to collaboratively train machine learning (ML) models without sharing their private data. Much of the existing work in FL fo…