research
∙
06/19/2023
Adaptive Federated Learning with Auto-Tuned Clients
Federated learning (FL) is a distributed machine learning framework wher...
share
research
∙
05/20/2023
On First-Order Meta-Reinforcement Learning with Moreau Envelopes
Meta-Reinforcement Learning (MRL) is a promising framework for training ...
research
∙
10/10/2022
On the Performance of Gradient Tracking with Local Updates
We study the decentralized optimization problem where a network of n age...
research
∙
10/03/2022
PersA-FL: Personalized Asynchronous Federated Learning
We study the personalized federated learning problem under asynchronous ...
research
∙
10/03/2022
Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation
Synchronous updates may compromise the efficiency of cross-device federa...
research
∙
04/18/2022
On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks
We study the decentralized consensus and stochastic optimization problem...
research
∙
04/16/2022
On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression
We study the acceleration of the Local Polynomial Interpolation-based Gr...
research
∙
04/01/2022
On Distributed Exact Sparse Linear Regression over Networks
In this work, we propose an algorithm for solving exact sparse linear re...
research
∙
03/22/2022
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
We propose a distributed Quantum State Tomography (QST) protocol, named ...
research
∙
03/14/2022
The Role of Local Steps in Local SGD
We consider the distributed stochastic optimization problem where n agen...
research
∙
01/30/2022
Faster Convergence of Local SGD for Over-Parameterized Models
Modern machine learning architectures are often highly expressive. They ...
research
∙
09/14/2021
Scalable Average Consensus with Compressed Communications
We propose a new decentralized average consensus algorithm with compress...
research
∙
06/28/2021
Robust Distributed Optimization With Randomly Corrupted Gradients
In this paper, we propose a first-order distributed optimization algorit...
research
∙
02/14/2021
Communication-Efficient Distributed Cooperative Learning with Compressed Beliefs
We study the problem of distributed cooperative learning, where a group ...
research
∙
11/20/2020
A General Framework for Distributed Inference with Uncertain Models
This paper studies the problem of distributed classification with a netw...
research
∙
11/06/2020
Communication-efficient Decentralized Local SGD over Undirected Networks
We consider the distributed learning problem where a network of n agents...
research
∙
10/20/2020
Robust Asynchronous and Network-Independent Cooperative Learning
We consider the model of cooperative learning via distributed non-Bayesi...
research
∙
07/07/2020
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks
We propose a distributed, cubic-regularized Newton method for large-scal...
research
∙
11/04/2019
Generalized Self-concordant Hessian-barrier algorithms
Many problems in statistical learning, imaging, and computer vision invo...
research
∙
09/10/2019
Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs
We study the problem of non-Bayesian social learning with uncertain mode...
research
∙
09/09/2019
Non-Bayesian Social Learning with Uncertain Models
Non-Bayesian social learning theory provides a framework that models dis...
research
∙
12/24/2018
On Increasing Self-Confidence in Non-Bayesian Social Learning over Time-Varying Directed Graphs
We study the convergence of the log-linear non-Bayesian social learning ...
research
∙
10/04/2018
Graph-Theoretic Analysis of Belief System Dynamics under Logic Constraints
Opinion formation cannot be modeled solely as an ideological deduction f...
research
∙
09/03/2018
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
We study the optimal convergence rates for distributed convex optimizati...
research
∙
06/11/2018
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
We study the problem of decentralized distributed computation of a discr...
research
∙
05/31/2018
On Curvature-aided Incremental Aggregated Gradient Methods
This paper studies an acceleration technique for incremental aggregated ...
research
∙
03/08/2018
Distributed Computation of Wasserstein Barycenters over Networks
We propose a new class-optimal algorithm for the distributed computation...
research
∙
12/01/2017
Optimal Algorithms for Distributed Optimization
In this paper, we study the optimal convergence rate for distributed con...
research
∙
04/10/2017
Distributed Learning for Cooperative Inference
We study the problem of cooperative inference where a group of agents in...
research
∙
09/23/2016
A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results
We overview some results on distributed learning with focus on a family ...
research
∙
09/19/2016
