Best-Effort Adaptation
This work addresses domain adaptation challenges for machine learning practitioners, but it appears incremental as it builds on existing discrepancy-based methods.
The paper tackles the problem of best-effort adaptation, aiming to create an accurate predictor for a target domain with moderate labeled data by leveraging a source domain with abundant labeled data, and reports experimental results demonstrating the effectiveness of their algorithms compared to baselines.
We study a problem of best-effort adaptation motivated by several applications and considerations, which consists of determining an accurate predictor for a target domain, for which a moderate amount of labeled samples are available, while leveraging information from another domain for which substantially more labeled samples are at one's disposal. We present a new and general discrepancy-based theoretical analysis of sample reweighting methods, including bounds holding uniformly over the weights. We show how these bounds can guide the design of learning algorithms that we discuss in detail. We further show that our learning guarantees and algorithms provide improved solutions for standard domain adaptation problems, for which few labeled data or none are available from the target domain. We finally report the results of a series of experiments demonstrating the effectiveness of our best-effort adaptation and domain adaptation algorithms, as well as comparisons with several baselines. We also discuss how our analysis can benefit the design of principled solutions for fine-tuning.