29 citations · 47 across the 8 of their papers we have counts for
8 papers
Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model
Jakob Assländer, Cem Gultekin, Andrew Mao +7
Purpose: To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model Theory and Methods: We combine two recently propo…
Random Geometric Graph: Some recent developments and perspectives
Quentin Duchemin, Yohann de Castro
The Random Geometric Graph (RGG) is a random graph model for network data with an underlying spatial representation. Geometry endows RGGs with a rich dependence structure and often…
SIGLE: a valid procedure for Selective Inference with the Generalized Linear Lasso
Quentin Duchemin, Yohann de Castro
This article investigates uncertainty quantification of the generalized linear lasso~(GLL), a popular variable selection method in high-dimensional regression settings. In many fie…
Cramér-Rao bound-informed training of neural networks for quantitative MRI
Xiaoxia Zhang, Quentin Duchemin, Kangning Liu +4
Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-li…
Three rates of convergence or separation via U-statistics in a dependent framework
Quentin Duchemin, Yohann De Castro, Claire Lacour
Despite the ubiquity of U-statistics in modern Probability and Statistics, their non-asymptotic analysis in a dependent framework may have been overlooked. In a recent work, a new…
Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains
Quentin Duchemin, Yohann de Castro, Claire Lacour
We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains. Working with bounded and -canonical kernels, we show that we can recov…
Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks
Quentin Duchemin, Yohann de Castro
We introduce Markov Random Geometric Graphs (MRGGs), a growth model for temporal dynamic networks. It is based on a Markovian latent space dynamic: consecutive latent points are sa…
Reliable Time Prediction in the Markov Stochastic Block Model
Quentin Duchemin
We introduce the Markov Stochastic Block Model (MSBM): a growth model for community based networks where node attributes are assigned through a Markovian dynamic. We rely on HMMs'…