Zhen Fang

How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

2020 · arXiv (Cornell University)

Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded …

Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms

2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from …

Learning to Augment Distributions for Out-of-Distribution Detection

2023 · arXiv (Cornell University)

Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD detection. Advanced works, despite their promising progress, may still fail in the open …

FEDGE: An Interference-Aware QoS Prediction Framework for Black-Box Scenario in IaaS Clouds with Domain Generalization

2024

Public cloud providers embrace multi-tenancy as a strategy to enhance the utilization and efficiency of resources. However, co-located virtual machines (VMs) suffer from qualityof-service (QoS) degradation caused by shared resource interference. Existing solutions for predicting …