  Active learning is a promising paradigm to reduce the labeling cost by
strategically requesting labels to improve model performance. However, existing
active learning methods often rely on expensive acquisition function to
compute, extensive modeling retraining and multiple rounds of interaction with
annotators. To address these limitations, we propose a novel approach for
active learning, which aims to select batches of unlabeled instances through a
learned surrogate model for data acquisition. A key challenge in this approach
is developing an acquisition function that generalizes well, as the history of
data, which forms part of the utility function's input, grows over time. Our
novel algorithmic contribution is a bilevel multi-task bilevel optimization
framework that predicts the relative utility -- measured by the validation
accuracy -- of different training sets, and ensures the learned acquisition
function generalizes effectively. For cases where validation accuracy is
expensive to evaluate, we introduce efficient interpolation-based surrogate
models to estimate the utility function, reducing the evaluation cost. We
demonstrate the performance of our approach through extensive experiments on
standard active classification benchmarks. By employing our learned utility
function, we show significant improvements over traditional techniques, paving
the way for more efficient and effective utility maximization in active
learning applications.

Machine Learning