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\title{Learning to Rank for Active Learning via Multi-Task Bilevel Optimization}

% Add authors
\author[1]{\href{mailto:<zixin@uchicago.edu>?Subject=Learning to Rank for Active Learning}{Zixin Ding}{}}
\author[2]{Si Chen}
\author[2]{Ruoxi Jia}
\author[1]{Yuxin Chen}
% Add affiliations after the authors
\affil[1]{%
    Department of Computer Science\\
    University of Chicago\\
    Chicago, Illinois, USA
}
\affil[2]{%
    Department of Computer Science\\
    Virginia Tech\\
    Blacksburg, Virginia, USA
}

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\begin{document}
\maketitle

\begin{abstract}
  Active learning is a promising paradigm for reducing labeling costs by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition functions, extensive model 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 \textit{learned surrogate model} for data acquisition. A key challenge in this approach is to develop 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 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. 
\end{abstract}

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\begin{acknowledgements} 
We are thankful to Feiyang Kang and Jiachen T. Wang for providing useful discussion and helpful feedback on the paper. YC and ZD acknowledge support from the National Science Foundation under Grant No. NSF IIS-2313131 and NSF FMRG-2037026. RJ and the ReDS lab acknowledge support through grants from the Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning, the National Science Foundation under Grant No. IIS-2312794, NSF IIS-2313130, NSF OAC-2239622, and the Commonwealth Cyber Initiative.

\end{acknowledgements}

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\appendix
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