Upcoming MAMS Colloquium Series

Spring 2026

3/27/2026, Friday 3:15-4:15 pm in Sears 439
Speaker: Professor Oh-Ran Kwon (The Ohio State University )
Title: Black-Box Knowledge Transfer for Distinct Feature Sets
Abstract: Pre-trained black-box predictive functions encode valuable knowledge, distilled from massive datasets and extensive computation. However, when the available input features come from an input space that differs from that of the black box, direct use is infeasible. A natural approach is to apply the black box through a mapping between the two input spaces, but this breaks down when the black box is highly nonlinear. Instead, in this talk, we introduce a method for transferring predictive knowledge from the black box to a different input space. Our approach decomposes the target prediction function into two components: a transferable component, which can be informed by the black box, and a non-transferable component, which captures information unique to the new space. We introduce a two-step estimation procedure aligned with this decomposition. We derive non-asymptotic prediction error bounds and show that transfer learning is advantageous over a non-transfer alternative, particularly when the non-transferable component is small or smooth. We further extend our approach to the case where multiple black-box functions are available and show that aggregating them provably improves predictive performance. Simulated and real data examples demonstrate the practical value of the proposed approach.