Cover Image

Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Montesuma, Eduardo Fernandes, Castellon, Fabiola Espinoza, Mboula, Fred Ngolè, Mayoue, Aurélien, Souloumiac, Antoine, Gouy-Pailler, Cédric
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
by: Montesuma, Eduardo Fernandes, et al.
Published: (2024)

Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
by: Clain, Rebecca, et al.
Published: (2025)

Optimal Transport for Domain Adaptation through Gaussian Mixture Models
by: Montesuma, Eduardo Fernandes, et al.
Published: (2024)

Recent Advances in Optimal Transport for Machine Learning
by: Montesuma, Eduardo Fernandes, et al.
Published: (2023)

Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning
by: Montesuma, Eduardo Fernandes, et al.
Published: (2024)

Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
by: Montesuma, Eduardo Fernandes, et al.
Published: (2023)

DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
by: Clain, Rebecca, et al.
Published: (2026)

Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)

Unsupervised Dataset Dictionary Learning for domain shift robust clustering: application to sitting posture identification
by: Hattay, Anas, et al.
Published: (2025)

Beyond Mapping : Domain-Invariant Representations via Spectral Embedding of Optimal Transport Plans
by: Saoud, Abdel Djalil Sad, et al.
Published: (2026)

On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach
by: Hattay, Anas, et al.
Published: (2026)

Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)

KD$^{2}$M: A unifying framework for feature knowledge distillation
by: Montesuma, Eduardo Fernandes
Published: (2025)

Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
by: Eufrazio, Rafael Pereira, et al.
Published: (2026)

A dimensionality reduction technique based on the Gromov-Wasserstein distance
by: Eufrazio, Rafael P., et al.
Published: (2025)

Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
by: Eufrazio, Rafael Pereira, et al.
Published: (2026)

Structured adaptive and random spinners for fast machine learning computations
by: Bojarski, Mariusz, et al.
Published: (2016)

ReBaPL: Repulsive Bayesian Prompt Learning
by: Bendou, Yassir, et al.
Published: (2025)

Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
by: Mellot, Apolline, et al.
Published: (2024)

Laplace Learning in Wasserstein Space
by: Oliver, Mary Chriselda Antony, et al.
Published: (2025)

Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space
by: Salmona, Antoine, et al.
Published: (2023)

Federated Adversarial Domain Adaptation
by: Peng, Xingchao, et al.
Published: (2019)

WassFFed: Wasserstein Fair Federated Learning
by: Han, Zhongxuan, et al.
Published: (2024)

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
by: Pallage, Julien, et al.
Published: (2024)

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection
by: Yehya, Jad, et al.
Published: (2025)

Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
by: Zeng, Zhichen, et al.
Published: (2025)

Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
by: Wei, Ting, et al.
Published: (2025)

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters
by: Pereira, Luiz, et al.
Published: (2025)

Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning
by: Zhang, Jingyuan, et al.
Published: (2024)

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)

Eliminating Domain Bias for Federated Learning in Representation Space
by: Zhang, Jianqing, et al.
Published: (2023)

Loss Gap Parity for Fairness in Heterogeneous Federated Learning
by: Erraji, Brahim, et al.
Published: (2026)

Sliced-Wasserstein Distance-based Data Selection
by: Pallage, Julien, et al.
Published: (2025)

Feature Diversification and Adaptation for Federated Domain Generalization
by: Yang, Seunghan, et al.
Published: (2024)

Inversion of Magnetic Data using Learned Dictionaries and Scale Space
by: Ahamed, Shadab, et al.
Published: (2025)

Inductive Domain Transfer In Misspecified Simulation-Based Inference
by: Senouf, Ortal, et al.
Published: (2025)

Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
by: Jiang, Enyi, et al.
Published: (2023)

Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series
by: Renault, Aurélien, et al.
Published: (2025)

Topological Dictionary Learning
by: Grimaldi, Enrico, et al.
Published: (2025)

Diverse Dictionary Learning
by: Zheng, Yujia, et al.
Published: (2026)