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

Keywords: Active Learning, Batch Active Learning, Generative Flow Networks, Deep Generative Modelling, Probabilistic Modelling

TL;DR: We construct highly informative batches for active learning efficiently by training a generative flow network to sample sets of data points proportional to a batch reward function.

Abstract: 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.

Submission Number: 36