Zhongle Xie

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 …

FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances

2026 · Proceedings of the ACM on Management of Data

Modern retrieval systems increasingly require integrating approximate nearest neighbor search (ANNS) with complex attribute filtering to handle hybrid queries in applications such as recommendation systems and retrieval-augmented generation (RAG). While HNSW-based inline-filtering methods show promise, …