Learning the Pareto Frontier of Predictive Models under Distribution Shift
Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks. These pretrained models can differ not only in performance but also in how they can be used: some only provide black-box predictions, while others may permit white-box access to internal representations that can be probed or fine-tuned. When deployed to the target domain in the presence of distribution shift, no single strategy, including zero-shot application, fine-tuning, or directly training a target-specific model, is uniformly the best. In this work, we propose Frontier Learning, a framework that treats a library of candidate models spanning different training hist...
| Authors | Yiming Dong, Jiwei Zhao, Yang Young Lu |
|---|---|
| Primary category | cs.LG |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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