Lidan Shou

BoKA: Bayesian Optimization based Knowledge Amalgamation for Multi-unknown-domain Text Classification

2024

With breakthroughs in pretrained language models, a large number of finetuned models specialized in distinct domains have surfaced online. Yet, when faced with a fresh dataset covering multiple (sub)domains, their performance might degrade. Reusing these …

CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning

2025 · IEEE Transactions on Knowledge and Data Engineering

Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it individually may still be insufficient due to restricted data diversity and …