26 citations · 61 across the 10 of their papers we have counts for
5 papers · 1 filter
On Distributed Adaptive Optimization with Gradient Compression
Xiaoyun Li, Belhal Karimi, Ping Li
We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce th…
A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent Variable Models
Belhal Karimi, Ping Li
The Expectation-Maximization (EM) algorithm is a popular choice for learning latent variable models. Variants of the EM have been initially introduced, using incremental updates to…
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching
Farzin Haddadpour, Belhal Karimi, Ping Li +1
Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices. In this…
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines +1
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because th…
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Belhal Karimi, Blazej Miasojedow, Eric Moulines +1
Stochastic approximation (SA) is a key method used in statistical learning. Recently, its non-asymptotic convergence analysis has been considered in many papers. However, most of t…