Search Results for author:
Found 13 papers, 2 papers with code
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges
To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory.
Motif-based tests for bipartite networks
Based on these results, we define a goodness-of-fit test for the B-EDD model and propose a family of tests for network comparisons.
Statistics Theory Statistics Theory
DeepLTRS: A Deep Latent Recommender System based on User Ratings and Reviews
The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information.
A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering
High-dimensional data clustering has become and remains a challenging task for modern statistics and machine learning, with a wide range of applications.
Image Denoising
Methodology
Cluster-Specific Predictions with Multi-Task Gaussian Processes
A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors' estimation of latent variables and processes.
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes
A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks.
Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL
We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes.
Exact Dimensionality Selection for Bayesian PCA
We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset.
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
To this end, using Roweis' probabilistic interpretation of PCA and a Gaussian prior on the loading matrix, we provide the first exact computation of the marginal likelihood of a Bayesian PCA model.
Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network.
Modelling time evolving interactions in networks through a non stationary extension of stochastic block models
To overcome this limitation, we propose a partition of the whole time horizon, in which interactions are observed, and develop a non stationary extension of the SBM, allowing to simultaneously cluster the nodes in a network along with fixed time intervals in which the interactions take place.
Graphs in machine learning: an introduction
In this paper, we give an introduction to some methods relying on graphs for learning.
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time varying intensity between nodes in unknown classes.
