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arXiv Machine Learning By Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira, Pedro Ribeiro, Pedro Saleiro, Pedro Bizarro, Carlos Soares

Transfer Learning for Evolving Domains

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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