1 code implementation • 22 Sep 2021 • Xiaoxia Zhang, Quentin Duchemin, Kangning Liu, Sebastian Flassbeck, Cem Gultekin, Carlos Fernandez-Granda, Jakob Assländer
We find, however, that in heterogeneous parameter spaces, i. e. in spaces in which the variance of the estimated parameters varies considerably, good performance is hard to achieve and requires arduous tweaking of the loss function, hyper parameters, and the distribution of the training data in parameter space.
no code implementations • 24 Jun 2021 • 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.
1 code implementation • 20 Nov 2020 • Quentin Duchemin, Yohann de Castro, Claire Lacour
We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains.
no code implementations • 12 Jun 2020 • Quentin Duchemin, Yohann de Castro
It is based on a Markovian latent space dynamic: consecutive latent points are sampled on the Euclidean Sphere using an unknown Markov kernel; and two nodes are connected with a probability depending on a unknown function of their latent geodesic distance.
1 code implementation • 9 Apr 2020 • 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.