libact: Pool-based Active Learning in Python
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Abstract (arXiv)
libact is a Python package designed to make active learning easier for general users. The package not only implements several popular active learning strategies, but also features the active-learning-by-learning meta-algorithm that assists the users to automatically select the best strategy on the fly. Furthermore, the package provides a unified interface for implementing more strategies, models and application-specific labelers. The package is open-source on Github, and can be easily installed from Python Package Index repository.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2017.
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Citing papers per year: 2021: 5; 2022: 3; 2023: 2; 2024: 1; 2025: 1.
12 papers.
- 2022A Lagrangian Duality Approach to Active Learningcited by 24
- 2021Small-Text: Active Learning for Text Classification in Pythoncited by 18
- 2021DeepAL: Deep Active Learning in Pythoncited by 13
- 2021Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Reviewcited by 11
- 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
- 2021Active Learning Under Malicious Mislabeling and Poisoning Attackscited by 3
- 2021Heuristic Stopping Rules For Technology-Assisted Reviewcited by 2
- 2022PyRelationAL: a python library for active learning research and developmentcited by 2
- 2023ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLPcited by 2
- 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AIcited by 0
- 2024regAL: Python Package for Active Learning of Regression Problemscited by 0
- 2025Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Gridcited by 0
- 2025Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid4 Jun 2025
- 2024regAL: Python Package for Active Learning of Regression Problems23 Oct 2024
- 2023ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP1 Aug 2023
- 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets15 Jun 2023
- 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI19 Jul 2022
- 2022PyRelationAL: a python library for active learning research and development23 May 2022
- 2022A Lagrangian Duality Approach to Active Learning8 Feb 2022
- 2021DeepAL: Deep Active Learning in Python30 Nov 2021
- 2021Small-Text: Active Learning for Text Classification in Python21 Jul 2021
- 2021Heuristic Stopping Rules For Technology-Assisted Review18 Jun 2021
- 2021Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review3 May 2021
- 2021Active Learning Under Malicious Mislabeling and Poisoning Attacks1 Jan 2021