2 citations · 2 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Fair In-Context Learning via Latent Concept Variables
Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu +3
The emerging in-context learning (ICL) ability of large language models (LLMs) has prompted their use for predictive tasks in various domains with different data types, including t…
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
Aneesh Komanduri, Chen Zhao, Feng Chen +1
Diffusion probabilistic models (DPMs) have become the state-of-the-art in high-quality image generation. However, DPMs have an arbitrary noisy latent space with no interpretable or…
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
Chen Zhao, Feng Mi, Xintao Wu +3
The fairness-aware online learning framework has emerged as a potent tool within the context of continuous lifelong learning. In this scenario, the learner's objective is to progre…