1 citations · 1 across the 5 of their papers we have counts for
5 papers
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization
Haoliang Wang, Chen Zhao, Feng Chen
Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unkn…
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning
Haoliang Wang, Chen Zhao, Feng Chen
Real-world machine learning applications often face simultaneous covariate and semantic shifts, challenging traditional domain generalization and out-of-distribution (OOD) detectio…
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization
Kai Jiang, Chen Zhao, Haoliang Wang +1
Generalizing to out-of-distribution data while being aware of model fairness is a significant and challenging problem in meta-learning. The goal of this problem is to find a set of…
Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
Chen Zhao, Kai Jiang, Xintao Wu +4
The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricat…
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
Haoliang Wang, Chen Zhao, Yunhui Guo +2
Two prevalent types of distributional shifts in machine learning are the covariate shift (as observed across different domains) and the semantic shift (as seen across different cla…