Výsledky vyhledávání - "Mirzaeifard, Reza"

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Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties

Autor: Mirzaeifard, Reza, Ghaderyan, Diyako, Werner, Stefan

Distributed sensors in the internet-of-things (IoT) generate vast amounts of sparse data. Analyzing this high-dimensional data and identifying relevant predictors pose substantial challenges, especially when data is preferred to remain on the device

Externí odkaz: http://arxiv.org/abs/2408.05640

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Decentralized Smoothing ADMM for Quantile Regression with Non-Convex Sparse Penalties

Autor: Mirzaeifard, Reza, Ghaderyan, Diyako, Werner, Stefan

In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limitations of existing methods, such as the sub-gradient approach, which fa

Externí odkaz: http://arxiv.org/abs/2408.01307

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Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties

Autor: Mirzaeifard, Reza, Venkategowda, Naveen K. D., Gogineni, Vinay Chakravarthi, Werner, Stefan

This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex nature of these problem

Externí odkaz: http://arxiv.org/abs/2309.03094

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Moreau Envelope ADMM for Decentralized Weakly Convex Optimization

Autor: Mirzaeifard, Reza, Venkategowda, Naveen K. D., Jung, Alexander, Werner, Stefan

This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in producing solutions that are close t

Externí odkaz: http://arxiv.org/abs/2308.16752

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Robust Networked Federated Learning for Localization

Autor: Mirzaeifard, Reza, Venkategowda, Naveen K. D., Werner, Stefan

This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of federated environments, distributed

Externí odkaz: http://arxiv.org/abs/2308.16737

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Structure Learning of Sparse GGMs over Multiple Access Networks

Autor: Tavassolipour, Mostafa, Karamzade, Armin, Mirzaeifard, Reza, Motahari, Seyed Abolfazl, Shalmani, Mohammad-Taghi Manzuri

A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The local machines can communicate with the central machine through a wireless

Externí odkaz: http://arxiv.org/abs/1812.10437

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