35 citations · 39 across the 7 of their papers we have counts for
7 papers
A principled approach to model validation in domain generalization
Boyang Lyu, Thuan Nguyen, Matthias Scheutz +2
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen dom…
Trade-off between reconstruction loss and feature alignment for domain generalization
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other…
Joint covariate-alignment and concept-alignment: a framework for domain generalization
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly…
Conditional entropy minimization principle for learning domain invariant representation features
Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2
Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG). Despite promising theory…
Barycentric-alignment and reconstruction loss minimization for domain generalization
Boyang Lyu, Thuan Nguyen, Prakash Ishwar +2
This paper advances the theory and practice of Domain Generalization (DG) in machine learning. We consider the typical DG setting where the hypothesis is composed of a representati…
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport
Shoaib Bin Masud, Boyang Lyu, Shuchin Aeron
In this paper, we extend the recently proposed multivariate rank energy distance, based on the theory of optimal transport, for statistical testing of distributional similarity, to…
Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS
Boyang Lyu, Thao Pham, Giles Blaney +4
Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to classify different levels of n-back tasks that invol…