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Found 14 papers, 3 papers with code
FMDA-OT: Federated Multi-source Domain Adaptation Through Optimal Transport
Multi-source Domain Adaptation (MDA) aims to adapt models trained on multiple labeled source domains to an unlabeled target domain.
Theoretical Guarantees for Domain Adaptation with Hierarchical Optimal Transport
To get to the bottom of this issue, we propose in this paper a new theoretical framework for domain adaptation through hierarchical optimal transport.
A quantum learning approach based on Hidden Markov Models for failure scenarios generation
Therefore, in this paper, we will study and compare the results of HQMMs and classical Hidden Markov Models HMM on a real datasets generated from real small systems in the field of PSA.
Convex Non-negative Matrix Factorization Through Quantum Annealing
In the second step we use an alternative strategy between the two QUBO problems corresponding to W and G to find the global solution.
Inductive Semi-supervised Learning Through Optimal Transport
In this paper, we tackle the inductive semi-supervised learning problem that aims to obtain label predictions for out-of-sample data.
Hierarchical Optimal Transport for Unsupervised Domain Adaptation
In this paper, we propose a novel approach for unsupervised domain adaptation, that relates notions of optimal transport, learning probability measures and unsupervised learning.
Label Propagation Through Optimal Transport
Our proposed approach is based on optimal transport, a mathematical theory that has been successfully used to address various machine learning problems, and is starting to attract renewed interest in semi-supervised learning community.
On the use of Wasserstein metric in topological clustering of distributional data
This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning.
Unsupervised collaborative learning using privileged information
In the collaborative clustering framework, the hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution.
Regularized Optimal Transport for Dynamic Semi-supervised Learning
In this paper, we propose a novel approach for the transductive semi-supervised learning, using a complete bipartite edge-weighted graph.
Selective information exchange in collaborative clustering using regularized Optimal Transport
Collaborative learning has recently achieved very significant results.
A survey on domain adaptation theory: learning bounds and theoretical guarantees
Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation.
Co-clustering through Optimal Transport
The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix.
Kernel Alignment for Unsupervised Transfer Learning
The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning.
