🤖 AI Summary
This work addresses the challenge in active learning where reliance on randomly initialized seed sets limits the ability to leverage related datasets for reducing annotation costs. To overcome this, the authors propose Active-Transfer Bagging (ATBagging), a novel approach that uniquely integrates transfer learning with Bagging ensembles. ATBagging estimates information gain via Bayesian predictive distributions and incorporates diversity in feature space through determinantal point processes (DPPs) for sample selection. By jointly optimizing both informativeness and diversity during both seed set construction and subsequent query stages, the method achieves consistent and significant improvements over existing techniques across four real-world datasets—QM9, ERA5, Forbes 2000, and Beijing PM2.5—particularly under low labeling budgets, where it substantially increases the area under the learning curve.
📝 Abstract
Modern machine learning has achieved remarkable success on many problems, but this success often depends on the existence of large, labeled datasets. While active learning can dramatically reduce labeling cost when annotations are expensive, early performance is frequently dominated by the initial seed set, typically chosen at random. In many applications, however, related or approximate datasets are readily available and can be leveraged to construct a better seed set. We introduce a new method for selecting the seed data set for active learning, Active-Transfer Bagging (ATBagging). ATBagging estimates the informativeness of candidate data point from a Bayesian interpretation of bagged ensemble models by comparing in-bag and out-of-bag predictive distributions from the labeled dataset, yielding an information-gain proxy. To avoid redundant selections, we impose feature-space diversity by sampling a determinantal point process (DPP) whose kernel uses Random Fourier Features and a quality-diversity factorization that incorporates the informativeness scores. This same blended method is used for selection of new data points to collect during the active learning phase. We evaluate ATBagging on four real-world datasets covering both target-transfer and feature-shift scenarios (QM9, ERA5, Forbes 2000, and Beijing PM2.5). Across seed sizes nseed = 10-100, ATBagging improves or ties early active learning and increases area under the learning-curve relative to alternative seed subset selection methodologies in almost all cases, with strongest benefits in low-data regimes. Thus, ATBagging provides a low-cost, high reward means to initiating active learning-based data collection.