7 papers
Generative Diffusion Models of Stochastic Graph Signals
YiÄit 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…
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…
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 Graph Neural Networks for Constrained Optimization
Samar Hadou, Alejandro Ribeiro
In this paper, we unroll the dynamics of the dual ascent (DA) algorithm in two coupled graph neural networks (GNNs) to solve constrained optimization problems. The two networks int…
Unrolled Neural Networks for Constrained Optimization
Samar Hadou, Alejandro Ribeiro
In this paper, we develop unrolled neural networks to solve constrained optimization problems, offering accelerated, learnable counterparts to dual ascent (DA) algorithms. Our fram…