1.3k citations · 3.7k across the 93 of their papers we have counts for
8 papers · 1 filter
Decentralized Multi-Task Stochastic Optimization With Compressed Communications
Navjot Singh, Xuanyu Cao, Suhas Diggavi +1
We consider a multi-agent network where each node has a stochastic (local) cost function that depends on the decision variable of that node and a random variable, and further the d…
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes
Qin Lu, Georgios B. Giannakis
Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous. The present paper takes a generative p…
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
Antonious M. Girgis, Deepesh Data, Suhas Diggavi
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy.…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
Kaan Ozkara, Navjot Singh, Deepesh Data +1
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face…
Dynamic group testing to control and monitor disease progression in a population
Sundara Rajan Srinivasavaradhan, Pavlos Nikolopoulos, Christina Fragouli +1
In the context of a pandemic like COVID-19, and until most people are vaccinated, proactive testing and interventions have been proved to be the only means to contain the disease s…