1 citations · 1 across the 10 of their papers we have counts for
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Colored Graph Signal Diffusion Models
Yiğit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
Standard diffusion processes with isotropic noise corruption are not spectrally neutral. They inherit a coarse-to-fine generation hierarchy from the nonuniform data spectrum, becau…
Generative Diffusion Models of Stochastic Graph Signals
Yiğit Berkay Uslu, Samar Hadou, Sergio Rozada +2
Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network…
Constrained Diffusion Models with Primal-Dual Inference
Samar Hadou, Yigit Berkay Uslu, Alejandro Ribeiro
This paper develops constrained diffusion models with primal-dual inference (PDI) to sample from optimal distributions of entropy-regularized optimization problems with \emph{avera…
Graph Signal Diffusion Models for Wireless Resource Allocation
Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
We consider constrained ergodic resource optimization in wireless networks with graph-structured interference. We train a diffusion model policy to match expert conditional distrib…
Unrolled Neural Networks for Constrained Optimization
Samar Hadou, Alejandro Ribeiro
We develop unrolled neural networks to solve constrained optimization problems, offering accelerated, learnable counterparts to dual ascent (DA) algorithms. Our framework, termed c…
A Constrained Optimization Perspective of Unrolled Transformers
Javier Porras-Valenzuela, Samar Hadou, Alejandro Ribeiro
We introduce a constrained optimization framework for training transformers that behave like optimization descent algorithms. Specifically, we enforce layerwise descent constraints…