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.

翻译：我们提出BatchGFN——一种新颖的基于池的主动学习方法，利用生成流网络按照批次奖励的比例采样数据点集。通过合适的奖励函数量化获取批次的效用（例如批次与模型参数之间的联合互信息），BatchGFN能够以原则性的方式构建信息量丰富的批次用于主动学习。我们证明，在玩具回归问题中，该方法能够在推理时对批次内每个点执行单次前向传播，即可采样接近最优效用的批次。这缓解了批次感知算法的计算复杂度，并消除了为寻找批次奖励最大化而采用贪心近似的需求。我们还展示了跨获取步骤摊销训练的初步结果，这将为扩展到实际任务奠定基础。