2 citations · 4 across the 8 of their papers we have counts for
8 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…
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Aneesh Komanduri, Xintao Wu, Yongkai Wu +1
Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by…