ICML 2025

AutoAL: Automated Active Learning with Differentiable Query Strategy Search
Yifeng Wang 1 Xueying Zhan 2 Siyu Huang 3
Abstract
As deep learning continues to evolve, the need
for data efficiency becomes increasingly impor-
tant. Considering labeling large datasets is both
time-consuming and expensive, active learning
(AL) provides a promising solution to this chal-
lenge by iteratively selecting the most informa-
tive subsets of examples to train deep neural net-
works, thereby reducing the labeling cost. How-
ever, the effectiveness of different AL algorithms
can vary significantly across data scenarios, and
determining which AL algorithm best fits a given
task remains a challenging problem. This work
presents the first differentiable AL strategy search
method, named AutoAL, which is designed on
top of existing AL sampling strategies. AutoAL
consists of two neural nets, named SearchNet
and FitNet, which are optimized concurrently un-
der a differentiable bi-level optimization frame-
work. For any given task, SearchNet and Fit-
Net are iteratively co-optimized using the la-
beled data, learning how well a set of candi-
date AL algorithms perform on that task. With
the optimal AL strategies identified, SearchNet
selects a small subset from the unlabeled pool
for querying their annotations, enabling efficient
training of the task model. Experimental results
demonstrate that AutoAL consistently achieves
superior accuracy compared to all candidate AL
algorithms and other selective AL approaches,
showcasing its potential for adapting and inte-
grating multiple existing AL methods across di-
verse tasks and domains. Code is available at:
https://github.com/haizailache999/AutoAL.
1Department of Electrical and Computer Engineering, Carnegie
Mellon University, Pittsburgh, USA 2Ray and Stephanie Lane
Computational Biology Department, Carnegie Mellon University,
Pittsburgh, USA 3Visual Computing Division, School of Com-
puting, Clemson University, Clemson, USA. Correspondence t...