Alain Rakotomamonjy | DeepAI

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∙ 06/07/2023

Adversarial Sample Detection Through Neural Network Transport Dynamics

We propose a detector of adversarial samples that is based on the view o...

0 Skander Karkar, et al. ∙

∙ 05/25/2023

Unifying GANs and Score-Based Diffusion as Generative Particle Models

Particle-based deep generative models, such as gradient flows and score-...

0 Jean-Yves Franceschi, et al. ∙

∙ 03/10/2023

Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

When dealing with electro or magnetoencephalography records, many superv...

0 Clément Bonet, et al. ∙

∙ 01/26/2023

Personalised Federated Learning On Heterogeneous Feature Spaces

Most personalised federated learning (FL) approaches assume that raw dat...

0 Alain Rakotomamonjy, et al. ∙

∙ 06/07/2022

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

The Sliced-Wasserstein distance (SW) is a computationally efficient and ...

12 Ruben Ohana, et al. ∙

∙ 05/19/2022

Diverse Weight Averaging for Out-of-Distribution Generalization

Standard neural networks struggle to generalize under distribution shift...

9 Alexandre Ramé, et al. ∙

∙ 02/01/2022

Generalizing to New Physical Systems via Context-Informed Dynamics Model

Data-driven approaches to modeling physical systems fail to generalize t...

7 Matthieu Kirchmeyer, et al. ∙

∙ 09/16/2021

Unsupervised domain adaptation with non-stochastic missing data

We consider unsupervised domain adaptation (UDA) for classification prob...

0 Matthieu Kirchmeyer, et al. ∙

∙ 07/05/2021

Differentially Private Sliced Wasserstein Distance

Developing machine learning methods that are privacy preserving is today...

0 Alain Rakotomamonjy, et al. ∙

∙ 06/07/2021

Photonic Differential Privacy with Direct Feedback Alignment

Optical Processing Units (OPUs) – low-power photonic chips dedicated to ...

7 Ruben Ohana, et al. ∙

∙ 11/19/2020

Wasserstein Learning of Determinantal Point Processes

Determinantal point processes (DPPs) have received significant attention...

0 Lucas Anquetil, et al. ∙

∙ 07/02/2020

Partial Trace Regression and Low-Rank Kraus Decomposition

The trace regression model, a direct extension of the well-studied linea...

0 Hachem Kadri, et al. ∙

∙ 06/24/2020

Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression

Owing to their statistical properties, non-convex sparse regularizers ha...

13 Alain Rakotomamonjy, et al. ∙

∙ 06/23/2020

Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport

The problem of domain adaptation on an unlabeled target dataset using kn...

0 Rosanna Turrisi, et al. ∙

∙ 06/20/2019

Screening Sinkhorn Algorithm for Regularized Optimal Transport

We introduce in this paper a novel strategy for efficiently approximate ...

0 Mokhtar Z. Alaya, et al. ∙

∙ 02/16/2019

Screening Rules for Lasso with Non-Convex Sparse Regularizers

Leveraging on the convexity of the Lasso problem , screening rules help ...

18 Alain Rakotomamonjy, et al. ∙

∙ 03/01/2018

Wasserstein Distance Measure Machines

This paper presents a distance-based discriminative framework for learni...

0 Alain Rakotomamonjy, et al. ∙

∙ 11/02/2017

Concave losses for robust dictionary learning

Traditional dictionary learning methods are based on quadratic convex lo...

0 Rafael Will M de Araujo, et al. ∙

∙ 05/24/2017

Joint Distribution Optimal Transportation for Domain Adaptation

This paper deals with the unsupervised domain adaptation problem, where ...

0 Nicolas Courty, et al. ∙

∙ 08/29/2016

Wasserstein Discriminant Analysis

Wasserstein Discriminant Analysis (WDA) is a new supervised method that ...

0 Rémi Flamary, et al. ∙

∙ 10/28/2015

Operator-valued Kernels for Learning from Functional Response Data

In this paper we consider the problems of supervised classification and ...

0 Hachem Kadri, et al. ∙

∙ 10/22/2015

Generalized conditional gradient: analysis of convergence and applications

The objectives of this technical report is to provide additional results...

0 Alain Rakotomamonjy, et al. ∙

∙ 07/02/2015

DC Proximal Newton for Non-Convex Optimization Problems

We introduce a novel algorithm for solving learning problems where both ...

0 Alain Rakotomamonjy, et al. ∙

∙ 01/12/2013

Functional Regularized Least Squares Classi cation with Operator-valued Kernels

Although operator-valued kernels have recently received increasing inter...

0 Hachem Kadri, et al. ∙

∙ 06/27/2012

Adaptive Canonical Correlation Analysis Based On Matrix Manifolds

In this paper, we formulate the Canonical Correlation Analysis (CCA) pro...

0 Florian Yger, et al. ∙

∙ 06/27/2012

Sparse Support Vector Infinite Push

In this paper, we address the problem of embedded feature selection for ...

0 Alain Rakotomamonjy, et al. ∙

∙ 03/07/2012

Multiple Operator-valued Kernel Learning

Positive definite operator-valued kernels generalize the well-known noti...

0 Hachem Kadri, et al. ∙

∙ 10/06/2009

Functional learning through kernels

This paper reviews the functional aspects of statistical learning theory...

0 Stéphane Canu, et al. ∙

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