PhD student, Chinese University of Hong Kong, The Chinese University of Hong Kong
1 paper 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.
