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07/20/2023
Graphs in State-Space Models for Granger Causality in Climate Science
Granger causality (GC) is often considered not an actual form of causali...
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07/06/2023
Sparse Graphical Linear Dynamical Systems
Time-series datasets are central in numerous fields of science and engin...
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03/22/2023
GraphIT: Iterative reweighted ℓ_1 algorithm for sparse graph inference in state-space models
State-space models (SSMs) are a common tool for modeling multi-variate d...
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01/04/2023
PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes
Denoising, detrending, deconvolution: usual restoration tasks, tradition...
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11/09/2022
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms
We study the variational inference problem of minimizing a regularized R...
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10/26/2022
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Standard few-shot benchmarks are often built upon simplifying assumption...
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10/19/2022
Gradient-based Adaptive Importance Samplers
Importance sampling (IS) is a powerful Monte Carlo methodology for the a...
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10/19/2022
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction
Co-administration of two or more drugs simultaneously can result in adve...
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10/03/2022
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Bayesian neural networks (BNNs) have received an increased interest in t...
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09/27/2022
Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density
This paper addresses the problem of image reconstruction for region-of-i...
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09/20/2022
Graphical Inference in Linear-Gaussian State-Space Models
State-space models (SSM) are central to describe time-varying complex sy...
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09/03/2022
A CNC approach for Directional Total Variation
The core of many approaches for the resolution of variational inverse pr...
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09/03/2022
A Variational Approach for Joint Image Recovery and Features Extraction Based on Spatially Varying Generalised Gaussian Models
The joint problem of reconstruction / feature extraction is a challengin...
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04/14/2022
Optimized Population Monte Carlo
Adaptive importance sampling (AIS) methods are increasingly used for the...
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10/14/2021
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
In this paper, we introduce a variational Bayesian algorithm (VBA) for i...
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04/21/2021
Deep Transform and Metric Learning Networks
Based on its great successes in inference and denosing tasks, Dictionary...
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11/09/2020
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems
This work proposes a supervised multi-channel time-series learning frame...
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11/09/2020
DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework
This work proposes an unsupervised fusion framework based on deep convol...
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11/09/2020
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
This work addresses the problem of analyzing multi-channel time series d...
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10/02/2020
Deep Convolutional Transform Learning – Extended version
This work introduces a new unsupervised representation learning techniqu...
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09/22/2020
DeepVir – Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19
This work formulates antiviral repositioning as a matrix completion prob...
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02/18/2020
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
On account of its many successes in inference tasks and denoising applic...
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02/06/2020
Block Distributed Majorize-Minimize Memory Gradient Algorithm and its application to 3D image restoration
Modern 3D image recovery problems require powerful optimization framewor...
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01/09/2020
GraphEM: EM algorithm for blind Kalman filtering under graphical sparsity constraints
Modeling and inference with multivariate sequences is central in a numbe...
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12/10/2019
Deep Latent Factor Model for Collaborative Filtering
Latent factor models have been used widely in collaborative filtering ba...
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12/10/2019
Transformed Subspace Clustering
Subspace clustering assumes that the data is sepa-rable into separate su...
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04/26/2019
General risk measures for robust machine learning
A wide array of machine learning problems are formulated as the minimiza...
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12/11/2018
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Variational methods are widely applied to ill-posed inverse problems for...
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12/04/2018
A probabilistic incremental proximal gradient method
In this paper, we propose a probabilistic optimization method, named pro...
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05/23/2018
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
In this paper, we develop a novel second-order method for training feed-...
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12/25/2017
A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
In this paper, we propose a new optimization algorithm for sparse logist...
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09/18/2017
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
This paper presents a fast approach for penalized least squares (LS) reg...
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10/24/2016