arXiv Machine Learning
Sep 14

Transfer Learning for Evolving Domains

The paper introduces Transfer Learning for Evolving Domains (TrED), a framework that models how data availability changes over time in real-world applications. TrED treats the entire trajectory of model updates as a single learning problem, rather than isolated snapshots, and defines a data availability process, a flexible learning protocol, and an evaluation criterion that scores the whole trajectory. The authors review existing transfer learning methods, noting that most are tailored to specific regimes and do not optimize the full trajectory, and argue that TrED is a well‑posed, unsolved research direction.

By Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira, Pedro Ribeiro, Pedro Saleiro, Pedro Bizarro, Carlos Soares
arXiv AI
Sep 17

Limits of Transfer Learning

The paper investigates the theoretical limits of transfer learning, demonstrating that careful selection of transferable information and its dependence on target problems is crucial. It establishes that the degree of probabilistic change in a transfer-learning algorithm imposes an upper bound on achievable improvement. These findings extend the algorithmic search framework to a broad class of learning tasks involving transfer.

By Jake Williams, Abel Tadesse, Tyler Sam, Huey Sun, George D. Montanez