2 citations · 2 across the 12 of their papers we have counts for
12 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…
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…
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…
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
Haoliang Wang, Chen Zhao, Xujiang Zhao +1
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels. While features at different layers cou…
Fairness-Aware Online Meta-learning
Chen Zhao, Feng Chen, Bhavani Thuraisingham
In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learnin…
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Krishnateja Killamsetty, Changbin Li, Chen Zhao +2
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, i…
Fair Meta-Learning For Few-Shot Classification
Chen Zhao, Changbin Li, Jincheng Li +1
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine le…
Unfairness Discovery and Prevention For Few-Shot Regression
Chen Zhao, Feng Chen
We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased da…