Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach

arXiv:2306.05843v2 Announce Type: replace
Abstract: Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation. This fixed approach is inefficient because of a dynamic trade-off between cost and speed -- larger batches are more costly, smaller batches lead to slower wall-clock run-times -- and the trade-off may change over the run (larger batches are often preferable earlier). To address this trade-off, we propose a novel Probabilistic Numerics framework that adaptively changes batch sizes. By …

abstract active learning arxiv cost cs.ai cs.lg cs.na dynamic experimentation math.na parallelization speed stat.co stat.ml trade trade-off type