2026
Learning Systems Expansion with Efficient Heterogeneity-aware Knowledge Transfer
AAAI 2026technical
Modern AI services must continually adapt to newly joined domains, yet delivering high-quality customized models is hampered by label sparsity, domain shifts, and tight budgets. We formulate this challenge as the learning system expansion problem and introduce HaT, an efficient heterogeneity-aware k