Transfer Learning with Kernel Methods

TL;DR

This paper introduces a transfer learning framework for kernel methods, enabling models trained on large datasets to be adapted effectively to new tasks, demonstrated through image classification and drug screening applications.

Contribution

The work proposes a novel transfer learning approach for kernel methods, including a theoretical analysis and practical validation on real-world tasks.

Findings

Transfer of kernels improves performance significantly over direct training.

Scaling laws describe transfer effectiveness as a function of target data size.

Theoretical derivation of exact scaling laws in a simplified linear setting.

Abstract

Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine learning models that are competitive on a variety of tasks, it has been unclear how to perform transfer learning for kernel methods. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. In particular, we show that transferring modern kernels trained on large-scale image datasets can result in substantial performance increase as compared to using the same kernel trained directly on the target task. In addition, we show that transfer-learned kernels allow a more accurate prediction of the effect of drugs on cancer cell lines. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws. By providing a simple and effective transfer learning framework for kernel methods, our work enables kernel methods trained on large datasets to be easily adapted to a variety of downstream target tasks.