# Learning Algorithms for Active Learning

arXiv: https://arxiv.org/abs/1708.00088 · HTML: https://mlsanity.com/paper/1708.00088/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 30 of 30)

- [Active Learning for Domain Adaptation: An Energy-Based Approach](https://mlsanity.com/paper/2112.01406/) (2021)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [How to Train Your MAML to Excel in Few-Shot Classification](https://mlsanity.com/paper/2106.16245/) (2021)
- [Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess](https://mlsanity.com/paper/2208.01366/) (2022)
- [Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing](https://mlsanity.com/paper/2101.05986/) (2021)
- [On Data Efficiency of Meta-learning](https://mlsanity.com/paper/2102.00127/) (2021)
- [Few-shot learning for medical text: A systematic review](https://mlsanity.com/paper/2204.14081/) (2022)
- [Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language](https://mlsanity.com/paper/2205.06111/) (2022)
- [On the Limitations of Simulating Active Learning](https://mlsanity.com/paper/2305.13342/) (2023)
- [How to distribute data across tasks for meta-learning?](https://mlsanity.com/paper/2103.08463/) (2021)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.14339/) (2021)
- [ImitAL: Learning Active Learning Strategies from Synthetic Data](https://mlsanity.com/paper/2108.07670/) (2021)
- [Budget-aware Few-shot Learning via Graph Convolutional Network](https://mlsanity.com/paper/2201.02304/) (2022)
- [Deep reinforced active learning for multi-class image classification](https://mlsanity.com/paper/2206.13391/) (2022)
- [Active Continual Learning: On Balancing Knowledge Retention and Learnability](https://mlsanity.com/paper/2305.03923/) (2023)
- [Learning to Rank for Active Learning via Multi-Task Bilevel Optimization](https://mlsanity.com/paper/2310.17044/) (2023)
- [Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees](https://mlsanity.com/paper/2402.02110/) (2024)
- [Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models](https://mlsanity.com/paper/2407.13989/) (2024)
- [Controlling Federated Learning for Covertness](https://mlsanity.com/paper/2308.08825/) (2023)
- [Active Reinforcement Learning Strategies for Offline Policy Improvement](https://mlsanity.com/paper/2412.13106/) (2024)
- [Performance-guided Reinforced Active Learning for Object Detection](https://mlsanity.com/paper/2601.15688/) (2026)
- [ImitAL: Learned Active Learning Strategy on Synthetic Data](https://mlsanity.com/paper/2208.11636/) (2022)
- [Active Learning for Argument Mining: A Practical Approach](https://mlsanity.com/paper/2109.13611/) (2021)
- [Fair Active Learning: Solving the Labeling Problem in Insurance](https://mlsanity.com/paper/2112.09466/) (2021)
- [Active Learning for Regression with Aggregated Outputs](https://mlsanity.com/paper/2210.01329/) (2022)
- [Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection](https://mlsanity.com/paper/2310.08387/) (2023)
- [Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Exploration](https://mlsanity.com/paper/2506.03469/) (2025)
- [Thompson Sampling-Based Learning and Control for Unknown Dynamic Systems](https://mlsanity.com/paper/2506.22186/) (2025)
- [ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation](https://mlsanity.com/paper/2608.01530/) (2026)

## Abstract (arXiv)

We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a method for constructing prediction functions from labeled training sets. Our model uses the item selection heuristic to gather labeled training sets from which to construct prediction functions. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.