Publications related to Rapid Scalable Distributed Power Flow with Open-Source Implementation

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Rachid Guerraoui, Nirupam Gupta, Youssef Allouah, Geovani Rizk, Rafaël Benjamin Pinot

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

2023

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Nirupam Gupta

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 LTD2022

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Alexandre Caboussat, Dimitrios Gourzoulidis

We consider a least-squares/relaxation finite element method for the numerical solution of the prescribed Jacobian equation. We look for its solution via a least-squares approach. We introduce a relaxation algorithm that decouples this least-squares proble ...

SPRINGER/PLENUM PUBLISHERS2022

Accelerated SGD for Non-Strongly-Convex Least Squares

Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre

We consider stochastic approximation for the least squares regression problem in the non-strongly convex setting. We present the first practical algorithm that achieves the optimal prediction error rates in terms of dependence on the noise of the problem, ...

2022

Rational-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots

Francesca Bonizzoni, Davide Pradovera

We introduce several spatially adaptive model order reduction approaches tailored to non-coercive elliptic boundary value problems, specifically, parametric-in-frequency Helmholtz problems. The offline information is computed by means of adaptive finite el ...

2021

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu

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 SOC2021

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre, Loucas Pillaud-Vivien

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 ...

2021

Frequency-Domain Diffusion Adaptation Over Networks

Ali H. Sayed, Ricardo Merched, Shuo Zhang

This paper analyzes the implementation of least-mean-squares (LMS)-based, adaptive diffusion algorithms over networks in the frequency-domain (FD). We focus on a scenario of noisy links and include a moving-average step for denoising after self-learning to ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Basic gestalt laws cannot explain uncrowding

Michael Herzog, Oh-Hyeon Choung, Einat Rashal

Visual crowding is the inability to perceive elements within clutter. Traditional crowding models, such as pooling, predict that performance deteriorates when flankers are added. However, this prediction has been disproved. For example, performance was fou ...

SAGE PUBLICATIONS LTD2019

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Rapid Scalable Distributed Power Flow with Open-Source Implementation

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Accelerated SGD for Non-Strongly-Convex Least Squares

Rational-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Frequency-Domain Diffusion Adaptation Over Networks

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Basic gestalt laws cannot explain uncrowding

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Chat with Graph Search

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Frequency-Domain Diffusion Adaptation Over Networks

Rational-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Basic gestalt laws cannot explain uncrowding

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Accelerated SGD for Non-Strongly-Convex Least Squares