364 citations · 1.8k across the 27 of their papers we have counts for
29 papers
A Structural FHMM for Interpretable Disease Trajectories in T2DM
Alessandro Mari, Ekaterina Krymova, Guillaume Obozinski +3
In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Type 2 diabetes mellitus (T2DM).…
From STEM-EDXS data to phase separation and quantification using physics-guided NMF
Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet +4
We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the…
Efficient algorithms for regularized Poisson Non-negative Matrix Factorization
Nathanaël Perraudin, Adrien Teutrie, Cécile Hébert +1
We consider the problem of regularized Poisson Non-negative Matrix Factorization (NMF) problem, encompassing various regularization terms such as Lipschitz and relatively smooth fu…
An evaluation of deep learning models for predicting water depth evolution in urban floods
Stefania Russo, Nathanaël Perraudin, Steven Stalder +4
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water…
Optirank: classification for RNA-Seq data with optimal ranking reference genes
Paola Malsot, Filipe Martins, Didier Trono +1
Classification algorithms using RNA-Sequencing (RNA-Seq) data as input are used in a variety of biological applications. By nature, RNA-Seq data is subject to uncontrolled fluctuat…
Robust detection and attribution of climate change under interventions
Enikő Székely, Sebastian Sippel, Nicolai Meinshausen +2
Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variabil…
Data-driven modeling of beam loss in the LHC
Ekaterina Krymova, Guillaume Obozinski, Michael Schenk +2
In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the pa…
Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide
Ekaterina Krymova, Benjamín Béjar, Dorina Thanou +7
Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor the evolution of the pandemic, inform the public, and assist governments in de…
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Shell Xu Hu, Pablo G. Moreno, Yang Xiao +4
We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the support set to generate a mo…
Tensor Decompositions for temporal knowledge base completion
Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier
Most algorithms for representation learning and link prediction in relational data have been designed for static data. However, the data they are applied to usually evolves with ti…
A direct approach to detection and attribution of climate change
Eniko Székely, Sebastian Sippel, Reto Knutti +2
We present here a novel statistical learning approach for detection and attribution (D&A) of climate change. Traditional optimal D&A studies try to directly model the observations…
Integer programming on the junction tree polytope for influence diagrams
Axel Parmentier, Victor Cohen, Vincent Leclère +2
Influence Diagrams (ID) are a flexible tool to represent discrete stochastic optimization problems, including Markov Decision Process (MDP) and Partially Observable MDP as standard…
Learning the effect of latent variables in Gaussian Graphical models with unobserved variables
Marina Vinyes, Guillaume Obozinski
The edge structure of the graph defining an undirected graphical model describes precisely the structure of dependence between the variables in the graph. In many applications, the…
Canonical Tensor Decomposition for Knowledge Base Completion
Timothée Lacroix, Nicolas Usunier, Guillaume Obozinski
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) see…
Tight convex relaxations for sparse matrix factorization
Emile Richard, Guillaume Obozinski, Jean-Philippe Vert
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and…
Domain adaptation for sequence labeling using hidden Markov models
Edouard Grave, Guillaume Obozinski, Francis Bach
Most natural language processing systems based on machine learning are not robust to domain shift. For example, a state-of-the-art syntactic dependency parser trained on Wall Stree…
Convex Relaxation for Combinatorial Penalties
Guillaume Obozinski, Francis Bach
In this paper, we propose an unifying view of several recently proposed structured sparsity-inducing norms. We consider the situation of a model simultaneously (a) penalized by a s…
On the Equivalence between Herding and Conditional Gradient Algorithms
Francis Bach, Simon Lacoste-Julien, Guillaume Obozinski
We show that the herding procedure of Welling (2009) takes exactly the form of a standard convex optimization algorithm--namely a conditional gradient algorithm minimizing a quadra…
Learning Hierarchical and Topographic Dictionaries with Structured Sparsity
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski +1
Recent work in signal processing and statistics have focused on defining new regularization functions, which not only induce sparsity of the solution, but also take into account th…
Group Lasso with Overlaps: the Latent Group Lasso approach
Guillaume Obozinski, Laurent Jacob, Jean-Philippe Vert
We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variables. We call the obtained formula…
Trace Lasso: a trace norm regularization for correlated designs
Edouard Grave, Guillaume Obozinski, Francis Bach
Using the -norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper,…
Structured sparsity through convex optimization
Francis Bach, Rodolphe Jenatton, Julien Mairal +1
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or…
Optimization with Sparsity-Inducing Penalties
Francis Bach, Rodolphe Jenatton, Julien Mairal +1
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extens…
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel +4
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning.…