How Many Samples Are Enough for Learning Across Domains?

Excerpt

arXiv:2609.39336v1 Announce Type: new Abstract: Understanding the fundamental mechanisms of learning is essential for designing systems with strong generalization. Recent studies have shown that increasing the number of training domains, or enlarging the distribution shift among them, improves generalization when each domain contains sufficiently many data samples. However, the conditions under which the data samples can be considered sufficient remain unexplored. In this work, we fill this gap