63 citations · 323 across the 46 of their papers we have counts for
59 papers
Competitive Networked Bivirus SIS spread over Hypergraphs
Sebin Gracy, Brian D. O. Anderson, Mengbin Ye +1
The paper deals with the spread of two competing viruses over a network of population nodes, accounting for pairwise interactions and higher-order interactions (HOI) within and bet…
Adaptive Federated Learning with Auto-Tuned Clients
Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe +1
Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients…
On First-Order Meta-Reinforcement Learning with Moreau Envelopes
Mohammad Taha Toghani, Sebastian Perez-Salazar, César A. Uribe
Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can quickly adapt to new environments and tasks. In this work, we study the MRL problem under th…
Towards Understanding the Endemic Behavior of a Competitive Tri-Virus SIS Networked Model
Sebin Gracy, Mengbin Ye, Brian D. O. Anderson +1
This paper studies the endemic behavior of a multi-competitive networked susceptible-infected-susceptible (SIS) model. Specifically, the paper deals with three competing virus syst…
Multi-Competitive Virus Spread over a Time-Varying Networked SIS Model with an Infrastructure Network
Sebin Gracy, Yuan Wang, Philip E. Pare +1
We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further en…
On the Performance of Gradient Tracking with Local Updates
Edward Duc Hien Nguyen, Sulaiman A. Alghunaim, Kun Yuan +1
We study the decentralized optimization problem where a network of agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly. State…
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics
Yuan Wang, Sebin Gracy, César A. Uribe +2
The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own he…
Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation
Mohammad Taha Toghani, César A. Uribe
Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases. The \textit{FedBuff} algorithm (Nguyen et al., 202…
PersA-FL: Personalized Asynchronous Federated Learning
Mohammad Taha Toghani, Soomin Lee, César A. Uribe
We study the personalized federated learning problem under asynchronous updates. In this problem, each client seeks to obtain a personalized model that simultaneously outperforms l…
On the Endemic Behavior of a Competitive Tri-Virus SIS Networked Model
Sebin Gracy, Mengbin Ye, Brian DO Anderson +1
This paper studies the endemic behavior of a multi-competitive networked susceptible-infected-susceptible (SIS) model. In particular, we focus on the case where there are three com…
Consensus ADMM-Based Distributed Simultaneous Imaging & Communication
Nishant Mehrotra, Ashutosh Sabharwal, César A. Uribe
This paper takes the first steps toward enabling wireless networks to perform both imaging and communication in a distributed manner. We propose Distributed Simultaneous Imaging an…
Distributed Generalized Wirtinger Flow for Interferometric Imaging on Networks
Sean M. Farrell, Ashok Veeraraghavan, Ashutosh Sabharwal +1
We study the problem of decentralized interferometric imaging over networks, where agents have access to a subset of local radar measurements and can compute pair-wise correlations…
On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks
Mohammad Taha Toghani, César A. Uribe
We study the decentralized consensus and stochastic optimization problems with compressed communications over static directed graphs. We propose an iterative gradient-based algorit…
On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression
Ekaterina Trimbach, Edward Duc Hien Nguyen, César A. Uribe
We study the acceleration of the Local Polynomial Interpolation-based Gradient Descent method (LPI-GD) recently proposed for the approximate solution of empirical risk minimization…
On Distributed Exact Sparse Linear Regression over Networks
Tu Anh-Nguyen, César A. Uribe
In this work, we propose an algorithm for solving exact sparse linear regression problems over a network in a distributed manner. Particularly, we consider the problem where data i…
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe +1
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix ov…
The Role of Local Steps in Local SGD
Tiancheng Qin, S. Rasoul Etesami, César A. Uribe
We consider the distributed stochastic optimization problem where agents want to minimize a global function given by the sum of agents' local functions, and focus on the hetero…
Faster Convergence of Local SGD for Over-Parameterized Models
Tiancheng Qin, S. Rasoul Etesami, César A. Uribe
Modern machine learning architectures are often highly expressive. They are usually over-parameterized and can interpolate the data by driving the empirical loss close to zero. We…
Scalable Average Consensus with Compressed Communications
Mohammad Taha Toghani, César A. Uribe
We propose a new decentralized average consensus algorithm with compressed communication that scales linearly with the network size n. We prove that the proposed method converges t…
Approximate Wasserstein Attraction Flows for Dynamic Mass Transport over Networks
Ferran Arqué, César A. Uribe, Carlos Ocampo-Martinez
This paper presents a Wasserstein attraction approach for solving dynamic mass transport problems over networks. In the transport problem over networks, we start with a distributio…
Robust Distributed Optimization With Randomly Corrupted Gradients
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all t…
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Pavel Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich +4
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in para…
Communication-efficient Distributed Cooperative Learning with Compressed Beliefs
Mohammad Taha Toghani, César A. Uribe
We study the problem of distributed cooperative learning, where a group of agents seeks to agree on a set of hypotheses that best describes a sequence of private observations. In t…
A General Framework for Distributed Inference with Uncertain Models
James Z. Hare, Cesar A. Uribe, Lance Kaplan +1
This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best descr…