International audienceWe investigate different ways of generating approximate solutions to the inverse Ising problem. Our approach consists in to take as a starting point for further perturbation procedures, a Bethe mean-field solution obtained with a maximum spanning tree of pairwise mutual information which we refer to as the "Bethe reference point". We consider three different ways of following this idea: in the first one, we discuss a greedy procedure by which optimal links to be added starting from the Bethe reference point are selected and calibrated iteratively; the second one is based on the observation that the natural gradient can be computed analytically at the Bethe point; the last one deals with loop corrections to the Bethe po...
To develop an efficient and accurate method for learning in the Ising model, we apply the tree-rewei...
International audienceWe survey some recent work where, motivated by traffic inference, we design in...
Inferring modelling parameters of dynamical processes from observational data is an important invers...
International audienceWe investigate different ways of generating approximate solutions to the inver...
We investigate different ways of generating approximate solutions to the inverse problem of pairwise...
The inverse Ising problem consists in inferring the coupling constants of an Ising model given the c...
We investigate different ways of generating approximate solutions to the pairwise Markov random fiel...
Paper submitted to Journal of Statistical PhysicsWe present a procedure to solve the inverse Ising p...
AbstractThe inverse Ising problem consists of taking a set of Ising configurations generated with un...
International audienceLarge scale inference problems of practical interest can often be addressed wi...
In this paper we solve the inverse problem for the cubic mean-field Ising model. Starting from confi...
6 pages, 5 figuresInternational audienceIn this work we explain how to properly use mean-field metho...
In this paper, we solve the inverse problem for the cubic mean-field Ising model. Starting from conf...
Abstract. I consider the problem of deriving couplings of a statistical model from mea-sured correla...
We study Ising chains with arbitrary multispin finite-range couplings, providing an explicit solutio...
To develop an efficient and accurate method for learning in the Ising model, we apply the tree-rewei...
International audienceWe survey some recent work where, motivated by traffic inference, we design in...
Inferring modelling parameters of dynamical processes from observational data is an important invers...
International audienceWe investigate different ways of generating approximate solutions to the inver...
We investigate different ways of generating approximate solutions to the inverse problem of pairwise...
The inverse Ising problem consists in inferring the coupling constants of an Ising model given the c...
We investigate different ways of generating approximate solutions to the pairwise Markov random fiel...
Paper submitted to Journal of Statistical PhysicsWe present a procedure to solve the inverse Ising p...
AbstractThe inverse Ising problem consists of taking a set of Ising configurations generated with un...
International audienceLarge scale inference problems of practical interest can often be addressed wi...
In this paper we solve the inverse problem for the cubic mean-field Ising model. Starting from confi...
6 pages, 5 figuresInternational audienceIn this work we explain how to properly use mean-field metho...
In this paper, we solve the inverse problem for the cubic mean-field Ising model. Starting from conf...
Abstract. I consider the problem of deriving couplings of a statistical model from mea-sured correla...
We study Ising chains with arbitrary multispin finite-range couplings, providing an explicit solutio...
To develop an efficient and accurate method for learning in the Ising model, we apply the tree-rewei...
International audienceWe survey some recent work where, motivated by traffic inference, we design in...
Inferring modelling parameters of dynamical processes from observational data is an important invers...