BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A. Malik Salem Lahlou Andrew Jesson Moksh Jain Nikolay Malkin Tristan Deleu Yoshua Bengio Yarin Gal
We introduce BatchGFN -- a novel approach for pool-based active learning that
uses generative flow networks to sample sets of data points proportional to a
batch reward. With an appropriate reward function to quantify the utility of
acquiring a batch, such as the joint mutual information between the batch and
the model parameters, BatchGFN is able to construct highly informative batches
for active learning in a principled way. We show our approach enables sampling
near-optimal utility batches at inference time with a single forward pass per
point in the batch in toy regression problems. This alleviates the
computational complexity of batch-aware algorithms and removes the need for
greedy approximations to find maximizers for the batch reward. We also present
early results for amortizing training across acquisition steps, which will
enable scaling to real-world tasks.