Learning Objective Adaptation by Correlation-Based Model Reuse

In open-environment machine learning (open ML), the learning objectives can vary according to specific real-world requirements. Models tailored for initial objectives may not be appropriate for the varied objectives. Retraining models from scratch for every single objective can be computationally intensive. Therefore, it is desirable to reuse models trained on the original objectives to help learn under the varied objectives. To this end, it is essential to characterize the objective correlations to better reuse the models. Previous works only consider the relative importance between pairs of previous and varied objectives, also known as previous-varied objectives correlations, ignoring correlations among the original objectives themselves. In this article, we demonstrate the importance of cross-original objective correlations. We propose a novel approach that employs the optimal transport technique to model correlations across all previous and varied objectives and then facilitates model reuse by utilizing learned transportation discrepancies to incorporate model reusabilities. Our empirical results show that our approach significantly outperforms existing benchmarks and well captures the underlying objective structure, validating the importance of accurate objective correlation modeling for learning with varied objectives.