# Understanding Goal-Oriented Active Learning via Influence Functions

arXiv: https://arxiv.org/abs/1905.13183 · HTML: https://mlsanity.com/paper/1905.13183/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 7 of 7)

- [Targeted Active Learning for Bayesian Decision-Making](https://mlsanity.com/paper/2106.04193/) (2021)
- [Influence Functions for Sequence Tagging Models](https://mlsanity.com/paper/2210.14177/) (2022)
- [Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes](https://mlsanity.com/paper/2309.05477/) (2023)
- [Few Clean Instances Help Denoising Distant Supervision](https://mlsanity.com/paper/2209.06596/) (2022)
- [Towards Efficient Active Learning in NLP via Pretrained Representations](https://mlsanity.com/paper/2402.15613/) (2024)
- [Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization](https://mlsanity.com/paper/2602.19578/) (2026)
- [Soft Strategy Selection for Batch-Mode Active Learning](https://mlsanity.com/paper/2610.05544/) (2026)

## Abstract (arXiv)

Active learning (AL) concerns itself with learning a model from as few labelled data as possible through actively and iteratively querying an oracle with selected unlabelled samples. In this paper, we focus on analyzing a popular type of AL in which the utility of a sample is measured by a specified goal achieved by the retrained model after accounting for the sample's marginal influence. Such AL strategies attract a lot of attention thanks to their intuitive motivations, yet they also suffer from impractically high computational costs due to their need for many iterations of model retraining. With the help of influence functions, we present an effective approximation that bypasses model retraining altogether, and propose a general efficient implementation that makes such AL strategies applicable in practice, both in the serial and the more challenging batch-mode setting. Additionally, we present both theoretical and empirical findings which call into question a few common practices and beliefs about such AL strategies.