DiMTE: Directed Model-Transfer Estimation for Wireless Datasets

Many data decisions depend on how well a model trained on one dataset would perform on another, such as which dataset to train on or which to add to scarce labels. Measuring this usually means training and testing one model per dataset. Dataset similarity measures are cheaper, but they are often symmetric and none returns the downstream loss. We introduce DiMTE, a directed transfer-risk estimator built from source-induced proxy predictors. DiMTE embeds every sample with a shared frozen encoder, such as a foundation model, fits a lightweight class-conditional proxy to each source, and evaluates it on labeled target samples under the deployment loss. It thus estimates how a model trained on each source would perform on the target, in the deployment metric. We prove a uniform bound that separates finite-sample error from the mismatch between proxy and deployment model. Across city-disjoint wireless benchmarks, including cities ray traced with an independent simulator after the method was frozen, DiMTE ranks sources best among methods that train no deployment model per source, and remains useful with few target labels. Estimators that inspect a trained model per source can be stronger, but only once those models exist. The same estimator also ranks camera-trap locations and source languages, with no wireless prior. DiMTE therefore lets a practitioner rank a whole library of datasets in the deployment metric before any per-source model is trained.