no code implementations • 26 Mar 2024 • Ashwin Aravind, Mohammad Taha Toghani, César A. Uribe
We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems.
no code implementations • 27 Nov 2023 • Delaram Pirhayatifard, Mohammad Taha Toghani, Guha Balakrishnan, César A. Uribe
In this work, we address the challenge of multi-task image generation with limited data for denoising diffusion probabilistic models (DDPM), a class of generative models that produce high-quality images by reversing a noisy diffusion process.
1 code implementation • 19 Jun 2023 • Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe, Anastasios Kyrillidis
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 without sharing their data.
no code implementations • 20 May 2023 • Mohammad Taha Toghani, Sebastian Perez-Salazar, César A. Uribe
We provide a detailed analysis of the MEMRL algorithm, where we show a sublinear convergence rate to a first-order stationary point for non-convex policy gradient optimization.
no code implementations • 3 Oct 2022 • Mohammad Taha Toghani, Soomin Lee, César A. Uribe
Our main technical contribution is a unified proof for asynchronous federated learning with bounded staleness that we apply to MAML and ME personalization frameworks.
no code implementations • 3 Oct 2022 • Mohammad Taha Toghani, César A. Uribe
Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases.
no code implementations • 18 Apr 2022 • Mohammad Taha Toghani, César A. Uribe
We study the decentralized consensus and stochastic optimization problems with compressed communications over static directed graphs.
no code implementations • 22 Mar 2022 • Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe, Anastasios Kyrillidis
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 over a set of local machines.
no code implementations • 14 Sep 2021 • 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 to the average of the initial values held locally by the agents of a network when agents are allowed to communicate with compressed messages.
no code implementations • 16 Jun 2021 • Junhyung Lyle Kim, Jose Antonio Lara Benitez, Mohammad Taha Toghani, Cameron Wolfe, Zhiwei Zhang, Anastasios Kyrillidis
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.
no code implementations • 14 Feb 2021 • 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.
no code implementations • 14 Nov 2020 • Mohammad Taha Toghani, Genevera I. Allen
We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration.