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02/20/2023
An evaluation of deep learning models for predicting water depth evolution in urban floods
In this technical report we compare different deep learning models for p...
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01/11/2023
Optirank: classification for RNA-Seq data with optimal ranking reference genes
Classification algorithms using RNA-Sequencing (RNA-Seq) data as input a...
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12/09/2022
Robust detection and attribution of climate change under interventions
Fingerprints are key tools in climate change detection and attribution (...
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08/18/2022
Data-driven modeling of beam loss in the LHC
In the Large Hadron Collider, the beam losses are continuously measured ...
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06/18/2021
Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide
Since the beginning of the COVID-19 pandemic, many dashboards have emerg...
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04/27/2020
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
We propose a meta-learning approach that learns from multiple tasks in a...
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04/10/2020
Tensor Decompositions for temporal knowledge base completion
Most algorithms for representation learning and link prediction in relat...
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10/08/2019
A direct approach to detection and attribution of climate change
We present here a novel statistical learning approach for detection and ...
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07/20/2018
Learning the effect of latent variables in Gaussian Graphical models with unobserved variables
The edge structure of the graph defining an undirected graphical model d...
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06/19/2018
Canonical Tensor Decomposition for Knowledge Base Completion
The problem of Knowledge Base Completion can be framed as a 3rd-order bi...
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07/19/2014
Tight convex relaxations for sparse matrix factorization
Based on a new atomic norm, we propose a new convex formulation for spar...
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05/06/2012
Convex Relaxation for Combinatorial Penalties
In this paper, we propose an unifying view of several recently proposed ...
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03/20/2012
On the Equivalence between Herding and Conditional Gradient Algorithms
We show that the herding procedure of Welling (2009) takes exactly the f...
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10/03/2011
Group Lasso with Overlaps: the Latent Group Lasso approach
We study a norm for structured sparsity which leads to sparse linear pre...
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09/12/2011
Structured sparsity through convex optimization
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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09/09/2011
Trace Lasso: a trace norm regularization for correlated designs
Using the ℓ_1-norm to regularize the estimation of the parameter vector ...
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08/03/2011
Optimization with Sparsity-Inducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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05/02/2011
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
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04/11/2011
Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured spa...
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09/11/2010
Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinat...
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08/31/2010
Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured spars...
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09/08/2009
Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, wh...
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08/05/2008
