# Data Shapley Valuation for Efficient Batch Active Learning

arXiv: https://arxiv.org/abs/2104.08312 · HTML: https://mlsanity.com/paper/2104.08312/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [A Distributional Framework for Data Valuation](https://mlsanity.com/paper/2002.12334/) (2020)
- [Exploring Representativeness and Informativeness for Active Learning](https://mlsanity.com/paper/1904.06685/) (2019)
- [Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?](https://mlsanity.com/paper/1911.07128/) (2019)

## Built on by (most cited first, 27 of 27)

- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification](https://mlsanity.com/paper/2211.06800/) (2022)
- [Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values](https://mlsanity.com/paper/2306.10165/) (2023)
- [Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution](https://mlsanity.com/paper/2401.15866/) (2024)
- [Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation](https://mlsanity.com/paper/2308.15709/) (2023)
- [Efficient Data Shapley for Weighted Nearest Neighbor Algorithms](https://mlsanity.com/paper/2401.11103/) (2024)
- [Fast Rates in Pool-Based Batch Active Learning](https://mlsanity.com/paper/2202.05448/) (2022)
- [TokenShapley: Token Level Context Attribution with Shapley Value](https://mlsanity.com/paper/2507.05261/) (2025)
- [DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation](https://mlsanity.com/paper/2306.02071/) (2023)
- [A Weighted K-Center Algorithm for Data Subset Selection](https://mlsanity.com/paper/2312.10602/) (2023)
- [Probably Approximate Shapley Fairness with Applications in Machine Learning](https://mlsanity.com/paper/2212.00630/) (2022)
- [Investigating Multi-source Active Learning for Natural Language Inference](https://mlsanity.com/paper/2302.06976/) (2023)
- [Survey of Computerized Adaptive Testing: A Machine Learning Perspective](https://mlsanity.com/paper/2404.00712/) (2024)
- [Less is more: Selecting informative and diverse subsets with balancing constraints](https://mlsanity.com/paper/2104.12835/) (2021)
- [Fairness-Aware Data Valuation for Supervised Learning](https://mlsanity.com/paper/2303.16963/) (2023)
- [ATOM: Attention Mixer for Efficient Dataset Distillation](https://mlsanity.com/paper/2405.01373/) (2024)
- [SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs](https://mlsanity.com/paper/2606.09886/) (2026)
- [A Comprehensive Study of Shapley Value in Data Analytics](https://mlsanity.com/paper/2412.01460/) (2024)
- [Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience](https://mlsanity.com/paper/2404.03084/) (2024)
- [Mining of Single-Class by Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2307.09109/) (2023)
- [Accelerated Shapley Value Approximation for Data Evaluation](https://mlsanity.com/paper/2311.05346/) (2023)
- [A case for data valuation transparency via DValCards](https://mlsanity.com/paper/2506.23349/) (2025)
- [Reliable and Private Utility Signaling for Data Markets](https://mlsanity.com/paper/2511.07975/) (2025)
- [Dynamic Shapley Computation](https://mlsanity.com/paper/2605.20620/) (2026)
- [Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation](https://mlsanity.com/paper/2607.03675/) (2026)

## Abstract (arXiv)

Annotating the right set of data amongst all available data points is a key challenge in many machine learning applications. Batch active learning is a popular approach to address this, in which batches of unlabeled data points are selected for annotation, while an underlying learning algorithm gets subsequently updated. Increasingly larger batches are particularly appealing in settings where data can be annotated in parallel, and model training is computationally expensive. A key challenge here is scale - typical active learning methods rely on diversity techniques, which select a diverse set of data points to annotate, from an unlabeled pool. In this work, we introduce Active Data Shapley (ADS) -- a filtering layer for batch active learning that significantly increases the efficiency of active learning by pre-selecting, using a linear time computation, the highest-value points from an unlabeled dataset. Using the notion of the Shapley value of data, our method estimates the value of unlabeled data points with regards to the prediction task at hand. We show that ADS is particularly effective when the pool of unlabeled data exhibits real-world caveats: noise, heterogeneity, and domain shift. We run experiments demonstrating that when ADS is used to pre-select the highest-ranking portion of an unlabeled dataset, the efficiency of state-of-the-art batch active learning methods increases by an average factor of 6x, while preserving performance effectiveness.