Mixed Semi-Supervised Generalized-Linear-Regression with Applications to Deep-Learning and Interpolators
This work addresses the challenge of leveraging unlabeled data for better regression accuracy, which is incremental as it extends existing semi-supervised learning approaches to generalized linear models and linear interpolators.
The paper tackles the problem of improving predictive performance in regression tasks by integrating unlabeled data into semi-supervised learning methods, proving that a nonzero mixing ratio consistently benefits performance and providing a framework to estimate the optimal ratio, with empirical demonstrations showing substantial improvements over standard supervised models.
We present a methodology for using unlabeled data to design semi-supervised learning (SSL) methods that improve the predictive performance of supervised learning for regression tasks. The main idea is to design different mechanisms for integrating the unlabeled data, and include in each of them a mixing parameter $α$, controlling the weight given to the unlabeled data. Focusing on Generalized Linear Models (GLM) and linear interpolators classes of models, we analyze the characteristics of different mixing mechanisms, and prove that it is consistently beneficial to integrate the unlabeled data with some nonzero mixing ratio $α>0$, in terms of predictive performance. Moreover, we provide a rigorous framework to estimate the best mixing ratio where mixed-SSL delivers the best predictive performance, while using the labeled and unlabeled data on hand. The effectiveness of our methodology in delivering substantial improvement compared to the standard supervised models, in a variety of settings, is demonstrated empirically through extensive simulation, providing empirical support for our theoretical analysis. We also demonstrate the applicability of our methodology (with some heuristic modifications) to improve more complex models, such as deep neural networks, in real-world regression tasks