6 citations · 16 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…