César A. Uribe

research

∙ 06/19/2023

Adaptive Federated Learning with Auto-Tuned Clients

Federated learning (FL) is a distributed machine learning framework wher...

0 Junhyung Lyle Kim, et al. ∙

research

∙ 05/20/2023

On First-Order Meta-Reinforcement Learning with Moreau Envelopes

Meta-Reinforcement Learning (MRL) is a promising framework for training ...

0 Mohammad Taha Toghani, et al. ∙

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...

0 Edward Duc Hien Nguyen, et al. ∙

research

∙ 10/03/2022

PersA-FL: Personalized Asynchronous Federated Learning

We study the personalized federated learning problem under asynchronous ...

9 Mohammad Taha Toghani, et al. ∙

research

∙ 10/03/2022

Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

Synchronous updates may compromise the efficiency of cross-device federa...

7 Mohammad Taha Toghani, et al. ∙

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...

1 Mohammad Taha Toghani, et al. ∙

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...

0 Ekaterina Trimbach, et al. ∙

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...

0 Tu Anh Nguyen, et al. ∙

research

∙ 03/22/2022

Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography

We propose a distributed Quantum State Tomography (QST) protocol, named ...

8 Junhyung Lyle Kim, et al. ∙

research

∙ 03/14/2022

The Role of Local Steps in Local SGD

We consider the distributed stochastic optimization problem where n agen...

0 Tiancheng Qin, et al. ∙

research

∙ 01/30/2022

Faster Convergence of Local SGD for Over-Parameterized Models

Modern machine learning architectures are often highly expressive. They ...

0 Tiancheng Qin, et al. ∙

research

∙ 09/14/2021

Scalable Average Consensus with Compressed Communications

We propose a new decentralized average consensus algorithm with compress...

13 Mohammad Taha Toghani, et al. ∙

research

∙ 06/28/2021

Robust Distributed Optimization With Randomly Corrupted Gradients

In this paper, we propose a first-order distributed optimization algorit...

0 Berkay Turan, et al. ∙

research

∙ 02/14/2021

Communication-Efficient Distributed Cooperative Learning with Compressed Beliefs

We study the problem of distributed cooperative learning, where a group ...

5 Mohammad Taha Toghani, et al. ∙

research

∙ 11/20/2020

A General Framework for Distributed Inference with Uncertain Models

This paper studies the problem of distributed classification with a netw...

3 James Z. Hare, et al. ∙

research

∙ 11/06/2020

Communication-efficient Decentralized Local SGD over Undirected Networks

We consider the distributed learning problem where a network of n agents...

0 Tiancheng Qin, et al. ∙

research

∙ 10/20/2020

Robust Asynchronous and Network-Independent Cooperative Learning

We consider the model of cooperative learning via distributed non-Bayesi...

0 Eduardo Mojica-Nava, et al. ∙

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...

10 César A. Uribe, et al. ∙

research

∙ 11/04/2019

Generalized Self-concordant Hessian-barrier algorithms

Many problems in statistical learning, imaging, and computer vision invo...

0 Pavel Dvurechensky, et al. ∙

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...

0 César A. Uribe, et al. ∙

research

∙ 09/09/2019

Non-Bayesian Social Learning with Uncertain Models

Non-Bayesian social learning theory provides a framework that models dis...

0 James Z. Hare, et al. ∙

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 ...

0 César A. Uribe, et al. ∙

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...

0 Angelia Nedić, et al. ∙

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...

0 César A. Uribe, et al. ∙

research

∙ 06/11/2018

Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters

We study the problem of decentralized distributed computation of a discr...

0 Pavel Dvurechensky, et al. ∙

research

∙ 05/31/2018

On Curvature-aided Incremental Aggregated Gradient Methods

This paper studies an acceleration technique for incremental aggregated ...

0 Hoi-To Wai, et al. ∙

research

∙ 03/08/2018

Distributed Computation of Wasserstein Barycenters over Networks

We propose a new class-optimal algorithm for the distributed computation...

0 César A. Uribe, et al. ∙

research

∙ 12/01/2017

Optimal Algorithms for Distributed Optimization

In this paper, we study the optimal convergence rate for distributed con...

0 César A. Uribe, et al. ∙

research

∙ 04/10/2017

Distributed Learning for Cooperative Inference

We study the problem of cooperative inference where a group of agents in...

0 Angelia Nedić, et al. ∙

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 ...

0 Angelia Nedić, et al. ∙

research

∙ 09/19/2016

Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes

A recent algorithmic family for distributed optimization, DIGing's, have...

0 Angelia Nedić, et al. ∙

César A. Uribe

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