Cover Image

Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Montesuma, Eduardo Fernandes, Stanc, Stevan Le, Mboula, Fred Ngolè
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

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

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

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

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

Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation
by: Montesuma, Eduardo Fernandes, et al.
Published: (2024)

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

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

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

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

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

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)

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)

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

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

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

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

Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
by: Huang, Min, et al.
Published: (2024)

Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization
by: Reichart, Larissa, et al.
Published: (2025)

Autonomous Source Knowledge Selection in Multi-Domain Adaptation
by: Li, Keqiuyin, et al.
Published: (2025)

Robust Multi-Source Domain Adaptation under Label Shift
by: Xu, Congbin, et al.
Published: (2025)

A General Representation-Based Approach to Multi-Source Domain Adaptation
by: Ng, Ignavier, et al.
Published: (2026)

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
by: Yuan, Jin, et al.
Published: (2022)

Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo
by: Pei, Jiangbo, et al.
Published: (2024)

Regularized Multi-output Gaussian Convolution Process with Domain Adaptation
by: Xinming, Wang, et al.
Published: (2024)

Analysis of Pseudo-Labeling for Online Source-Free Universal Domain Adaptation
by: Schlachter, Pascal, et al.
Published: (2025)

Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation
by: Gong, Chenqi, et al.
Published: (2025)

Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
by: Liu, Yuhang, et al.
Published: (2022)

Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation
by: Sun, Hankang, et al.
Published: (2025)

GMM-COMET: Continual Source-Free Universal Domain Adaptation via a Mean Teacher and Gaussian Mixture Model-Based Pseudo-Labeling
by: Schlachter, Pascal, et al.
Published: (2026)

POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
by: Wang, Junxiang, et al.
Published: (2023)

Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation
by: Zhang, Zhen, et al.
Published: (2025)

Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
by: Ma, Yao, et al.
Published: (2024)

Enhancing Domain Adaptation through Prompt Gradient Alignment
by: Phan, Hoang, et al.
Published: (2024)

Multi-Source to Multi-Target Decentralized Federated Domain Adaptation
by: Wang, Su, et al.
Published: (2023)

Data-Efficient and Robust Trajectory Generation through Pathlet Dictionary Learning
by: Tang, Yuanbo, et al.
Published: (2025)

DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition
by: Hu, Fo, et al.
Published: (2025)

Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
by: Wang, Zhenyu, et al.
Published: (2023)

Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation
by: Ying, Chao, et al.
Published: (2025)

RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts
by: Truong, Tuan, et al.
Published: (2025)