Assistant Professor, Department of Computer Science, The University of Hong Kong
2 papers at NeurIPS 2025
We propose TreeSynth, a tree-guided subspace-based data synthesis approach, achieving superior data diversity, model performance, robust scalability, and data balance efficacy.