1 citations · 1 across the 4 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…