Title: Performance-Enhanced Aggregated Representation Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: representation learning, model averaging, machine learning

Abstract: Machine learning models rely on representation learning to transform complex data into a simpler and more structured form for analysis. However, the performance of these models depends on how well the data is represented, and no single method can consistently capture all the important information needed for downstream tasks. This study introduces a generalized model averaging framework that integrates multiple representation learning techniques, enabling researchers to combine advanced machine learning models to enhance downstream task performance. By assigning optimal weights to different representations, the proposed approach not only achieves numerical efficiency but also provides theoretical guarantees, ensuring its optimality in real-world scenarios.

TL;DR: A theory-guaranteed framework that optimally weights multiple representations to enhance downstream task performance.