Search Results for author: Emilie Chouzenoux

Found 30 papers, 10 papers with code

Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization

1 code implementation25 Oct 2023 Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Víctor Elvira

We then show that the $\alpha$-divergence can be approximated by a generalized notion of effective sample size and leverage this new perspective to adapt the tail parameter with Bayesian optimization.

Bayesian Optimization Variational Inference

Aggregated f-average Neural Network for Interpretable Ensembling

no code implementations9 Oct 2023 Mathieu Vu, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed

Ensemble learning leverages multiple models (i. e., weak learners) on a common machine learning task to enhance prediction performance.

Ensemble Learning Few-Shot Class-Incremental Learning +1

Majorization-Minimization for sparse SVMs

no code implementations31 Aug 2023 Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini, Salla Latva-Aijo, Dominik Narnhofer, Jean-Christophe Pesquet, Sebastian J. Scott, Mahsa Yousefi

Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework.

Binary Classification

Sparse Graphical Linear Dynamical Systems

no code implementations6 Jul 2023 Emilie Chouzenoux, Victor Elvira

This work proposes a novel approach to fill this gap by introducing a joint graphical modeling framework that bridges the static graphical Lasso model and a causal-based graphical approach for the linear-Gaussian SSM.

Time Series

Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference

1 code implementation26 Oct 2022 Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed

We relax these assumptions and extend current benchmarks, so that the query-set classes of a given task are unknown, but just belong to a much larger set of possible classes.

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

no code implementations19 Oct 2022 Stuti Jain, Emilie Chouzenoux, Kriti Kumar, Angshul Majumdar

The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques.

Matrix Completion

Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density

no code implementations27 Sep 2022 Marion Savanier, Emilie Chouzenoux, Jean-Christophe Pesquet, Cyril Riddell

We unfold the Dual Block coordinate Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner.

Computed Tomography (CT) Image Reconstruction

Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

1 code implementation14 Oct 2021 Yunshi Huang, Emilie Chouzenoux, Jean-Christophe Pesquet

In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution.

Rolling Shutter Correction

Deep Transform and Metric Learning Networks

no code implementations21 Apr 2021 Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim

Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.

Dictionary Learning Metric Learning

DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework

no code implementations9 Nov 2020 Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia

This work proposes an unsupervised fusion framework based on deep convolutional transform learning.

Deep Convolutional Transform Learning -- Extended version

no code implementations2 Oct 2020 Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia

This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL).

BIG-bench Machine Learning Clustering +1

DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19

1 code implementation22 Sep 2020 Aanchal Mongia, Stuti Jain, Emilie Chouzenoux, Angshul Majumda

This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns.

Matrix Completion

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

1 code implementation3 Jul 2020 Aanchal Mongia, Sanjay Kr. Saha, Emilie Chouzenoux, Angshul Majumdar

The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals.

Quantitative Methods

Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks

no code implementations18 Feb 2020 Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim

On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.

Denoising Dictionary Learning +1

Transformed Subspace Clustering

no code implementations10 Dec 2019 Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux

We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces.

Benchmarking Clustering +2

General risk measures for robust machine learning

no code implementations26 Apr 2019 Emilie Chouzenoux, Henri Gérard, Jean-Christophe Pesquet

A wide array of machine learning problems are formulated as the minimization of the expectation of a convex loss function on some parameter space.

BIG-bench Machine Learning

Deep Unfolding of a Proximal Interior Point Method for Image Restoration

1 code implementation11 Dec 2018 Carla Bertocchi, Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Marco Prato

Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution.

Deblurring Image Deblurring +1

A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression

no code implementations25 Dec 2017 Luis M. Briceno-Arias, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet

In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method.

regression

A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior

no code implementations18 Sep 2017 Qi Wei, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet

This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior.

regression

A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise

no code implementations24 Oct 2016 Yosra Marnissi, Yuling Zheng, Emilie Chouzenoux, Jean-Christophe Pesquet

We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise.

Deblurring Image Deblurring +1

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