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A new method for solving broadband matching problems
Related publications (32)
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EPFL2024Random matrix methods for high-dimensional machine learning models
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MOX Modeling and Scientific Computing2023Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
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The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...
2022Statistical limits of dictionary learning: Random matrix theory and the spectral replica method
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We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...
AMER PHYSICAL SOC2022Optimal Matching of Random Parts
This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...
2022Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem
This paper considers the multi-agent linear least-squares problem in a server-agent network architecture. The system comprises multiple agents, each with a set of local data points. The agents are connected to a server, and there is no inter-agent communic ...
PERGAMON-ELSEVIER SCIENCE LTD2022Last iterate convergence of SGD for Least-Squares in the Interpolation regime
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Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...
2021Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
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Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...
IEEE COMPUTER SOC2021Feedback and Common Information: Bounds and Capacity for Gaussian Networks
Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...
EPFL2021