Publications related to Partial least squares regression

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

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

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

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

Unlocking crowding by ensemble statistics

Michael Herzog, David Pascucci, Oh-Hyeon Choung, Yury Markov, Natalia Tiurina

In crowding,1-7 objects that can be easily recognized in isolation appear jumbled when surrounded by other elements.8 Traditionally, crowding is explained by local pooling mechanisms,3,6,9-15 but many findings have shown that the global configuration of th ...

CELL PRESS2022

Random Surface Covariance Estimation by Shifted Partial Tracing

Victor Panaretos, Tomas Masák

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even ...

TAYLOR & FRANCIS INC2022

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

Edoardo Charbon, Andrei Ardelean, Ming-Lo Wu, Kazuhiro Morimoto

Advances in high-speed imaging techniques have opened new possibilities for capturing ultrafast phenomena such as light propagation in air or through media. Capturing light in flight in three-dimensional xyt space has been reported based on various types o ...

AMER PHYSICAL SOC2021

A thorough investigation of photo-catalytic degradation of ortho and para-nitro phenols in binary mixtures: new insights into evaluating degradation progress using chemometrics approaches

Sayyed Hashem Sajjadi

In this study, photocatalytic degradation of 2-nitrophenol and 4-nitrophenol were carried out efficiently using ZnO nanoparticles photo-catalyst under simulated solar irradiation. The photo-decomposition processes were optimized simultaneously by employing ...

ROYAL SOC CHEMISTRY2021

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

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Partial least squares regression

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

Accelerated SGD for Non-Strongly-Convex Least Squares

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

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

Unlocking crowding by ensemble statistics

Random Surface Covariance Estimation by Shifted Partial Tracing

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

A thorough investigation of photo-catalytic degradation of ortho and para-nitro phenols in binary mixtures: new insights into evaluating degradation progress using chemometrics approaches

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

Hydrogel Electrolytes Based on Xanthan Gum: Green Route towards Stable Dye-Sensitized Solar Cells

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Chat with Graph Search

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

A thorough investigation of photo-catalytic degradation of ortho and para-nitro phenols in binary mixtures: new insights into evaluating degradation progress using chemometrics approaches

Accelerated SGD for Non-Strongly-Convex Least Squares

Random Surface Covariance Estimation by Shifted Partial Tracing

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

Unlocking crowding by ensemble statistics

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

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

Hydrogel Electrolytes Based on Xanthan Gum: Green Route towards Stable Dye-Sensitized Solar Cells