6 citations · 16 across the 13 of their papers we have counts for
13 papers
A Moreau Envelope Approach for LQR Meta-Policy Estimation
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. We propose a Moreau Envelope-…
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
Delaram Pirhayatifard, Mohammad Taha Toghani, Guha Balakrishnan +1
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 produ…
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…
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 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…
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…
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…
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Junhyung Lyle Kim, JA Lara Benitez, Mohammad Taha Toghani +3
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.…
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…
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
Mohammad Taha Toghani, Genevera I. Allen
Boosting methods are among the best general-purpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting metho…
BlockMarkchain: A Secure Decentralized Data Market with a Constant Load on the Blockchain
Hamidreza Ehteram, Mohammad Taha Toghani, Mohammad Ali Maddah-Ali
In this paper, we develop BlockMarkchain, as a secure data market place, where individual data sellers can exchange certified data with buyers, in a secure environment, without any…