1 citations · 1 across the 12 of their papers we have counts for
13 papers
Gromov-Wasserstein Distillation for Inductive Multi-View Embedding
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing no explicit mapping for unseen…
Multi-Domain Clustering via Measure Quantization
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Clustering is a fundamental task in data analysis, typically addressed through centroid-based methods such as K-means. In this work, we present a general framework for multi-domain…
DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngole Mboula
Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target domain in a decentralized set…
Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromov-Wasserstein-…
Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Classical approaches often rely o…
ReBaPL: Repulsive Bayesian Prompt Learning
Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma +3
Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to…
Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
Eduardo Fernandes Montesuma, Yassir Bendou, Mike Gartrell
Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space. Existing discrete methods are not…
Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngolè Mboula
Decentralized Multi-Source Domain Adaptation (DMSDA) is a challenging task that aims to transfer knowledge from multiple related and heterogeneous source domains to an unlabeled ta…
A dimensionality reduction technique based on the Gromov-Wasserstein distance
Rafael P. Eufrazio, Eduardo Fernandes Montesuma, Charles C. Cavalcante
Analyzing relationships between objects is a pivotal problem within data science. In this context, Dimensionality reduction (DR) techniques are employed to generate smaller and mor…
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning
Eduardo Fernandes Montesuma, Stevan Le Stanc, Fred Ngolè Mboula
This paper addresses the challenge of online multi-source domain adaptation (MSDA) in transfer learning, a scenario where one needs to adapt multiple, heterogeneous source domains…
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation
Eduardo Fernandes Montesuma, Fabiola Espinoza Castellon, Fred Ngolè Mboula +3
Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset. Conventiona…
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning
Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac
In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first con…
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
Fabiola Espinoza Castellon, Eduardo Fernandes Montesuma, Fred Ngolè Mboula +3
In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framew…