Transfer Learning via $\ell_1$ Regularization
This work addresses the challenge of model adaptation in changing environments for real-world applications, representing an incremental improvement over existing methods.
The paper tackles the problem of adapting machine learning models to nonstationary environments by proposing a transfer learning method using ℓ1 regularization to balance stability and plasticity, achieving effective adaptation with theoretical guarantees.
Machine learning algorithms typically require abundant data under a stationary environment. However, environments are nonstationary in many real-world applications. Critical issues lie in how to effectively adapt models under an ever-changing environment. We propose a method for transferring knowledge from a source domain to a target domain via $\ell_1$ regularization. We incorporate $\ell_1$ regularization of differences between source parameters and target parameters, in addition to an ordinary $\ell_1$ regularization. Hence, our method yields sparsity for both the estimates themselves and changes of the estimates. The proposed method has a tight estimation error bound under a stationary environment, and the estimate remains unchanged from the source estimate under small residuals. Moreover, the estimate is consistent with the underlying function, even when the source estimate is mistaken due to nonstationarity. Empirical results demonstrate that the proposed method effectively balances stability and plasticity.