11 citations · 25 across the 6 of their papers we have counts for
7 papers
Recognizable Series on Hypergraphs
Raphaël Bailly, François Denis, Guillaume Rabusseau
We introduce the notion of Hypergraph Weighted Model (HWM) that generically associates a tensor network to a hypergraph and then computes a value by tensor contractions directed by…
Learning Negative Mixture Models by Tensor Decompositions
Guillaume Rabusseau, François Denis
This work considers the problem of estimating the parameters of negative mixture models, i.e. mixture models that possibly involve negative weights. The contributions of this paper…
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
François Denis, Mattias Gybels, Amaury Habrard
Learning probabilistic models over strings is an important issue for many applications. Spectral methods propose elegant solutions to the problem of inferring weighted automata fro…
On Probability Distributions for Trees: Representations, Inference and Learning
François Denis, Amaury Habrard, Rémi Gilleron +2
We study probability distributions over free algebras of trees. Probability distributions can be seen as particular (formal power) tree series [Berstel et al 82, Esik et al 03], i.…
Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference
Amaury Habrard, Francois Denis, Yann Esposito
In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some cl…
Rational stochastic languages
François Denis, Yann Esposito
The goal of the present paper is to provide a systematic and comprehensive study of rational stochastic languages over a semiring K \in {Q, Q +, R, R+}. A rational stochastic langu…