On the impact of measure pre-conditionings on general parametric ML models and transfer learning via domain adaptation

arXiv:2403.02432v1 Announce Type: cross
Abstract: We study a new technique for understanding convergence of learning agents under small modifications of data. We show that such convergence can be understood via an analogue of Fatou's lemma which yields gamma-convergence. We show it's relevance and applications in general machine learning tasks and domain adaptation transfer learning.

abstract agents arxiv convergence cs.lg data domain domain adaptation general impact math.oc ml models parametric show small stat.ml study transfer transfer learning type understanding via