Publications related to Reprint of: Weak epsilon-nets have basis of size O(1/epsilonlog(1/epsilon)) in any dimension

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Rachid Guerraoui, Anne-Marie Kermarrec, Sadegh Farhadkhani, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

2023

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Gabriel Okasa

Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sam ...

2022

Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

2020

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019

CASSIS: Characterization with Adaptive Sample-Size Inferential Statistics Applied to Inexact Circuits

Vincent Frédéric Camus

To design faster and more energy-efficient systems, numerous inexact arithmetic operators have been proposed, generally obtained by modifying the logic structure of conventional circuits. However, as the quality of service of an application has to be ensur ...

2018

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...

Elsevier2018

Nonlinear PD control of a Parallel Delta robot: Expermentals Results

Mohamed Bouri

In this paper, a nonlinear proportional derivative control (NPD) is proposed to solve the trajectory tracking problem of a parallel Delta robot. In order to handle the strong coupling effect and external disturbances as well as to realise high-precision tr ...

IEEE2018

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

MATHICSE2017

Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

2017

Reprint of: Weak epsilon-nets have basis of size O(1/epsilonlog(1/epsilon)) in any dimension

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Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Multilevel weighted least squares polynomial approximation

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

CASSIS: Characterization with Adaptive Sample-Size Inferential Statistics Applied to Inexact Circuits

Random sampling of bandlimited signals on graphs

Nonlinear PD control of a Parallel Delta robot: Expermentals Results

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Multilevel weighted least squares polynomial approximation

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Nonlinear PD control of a Parallel Delta robot: Expermentals Results

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Multilevel weighted least squares polynomial approximation

Random sampling of bandlimited signals on graphs

CASSIS: Characterization with Adaptive Sample-Size Inferential Statistics Applied to Inexact Circuits

Multilevel weighted least squares polynomial approximation