mlsanityData to 7 Oct 2026

A Benchmark and Comparison of Active Learning for Logistic Regression

arXiv 1611.08618 ↗First posted 25 Nov 2016stat.ML cs.LGcited by 36

Not on the map. Why?

Abstract (arXiv)

Logistic regression is by far the most widely used classifier in real-world applications. In this paper, we benchmark the state-of-the-art active learning methods for logistic regression and discuss and illustrate their underlying characteristics. Experiments are carried out on three synthetic datasets and 44 real-world datasets, providing insight into the behaviors of these active learning methods with respect to the area of the learning curve (which plots classification accuracy as a function of the number of queried examples) and their computational costs. Surprisingly, one of the earliest and simplest suggested active learning methods, i.e., uncertainty sampling, performs exceptionally well overall. Another remarkable finding is that random sampling, which is the rudimentary baseline to improve upon, is not overwhelmed by individual active learning techniques in many cases.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 9; 2023: 8; 2024: 5; 2025: 3; 2026: 2.

36 papers.

Sort:

By area:

  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2021Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answeringcited by 62
  3. 2021Is segmentation uncertainty useful?cited by 37
  4. 2022Uncertainty Estimation for Language Reward Modelscited by 35
  5. 2022Bayesian Active Learning with Fully Bayesian Gaussian Processescited by 26
  6. 2024Active Learning for Neural PDE Solverscited by 22
  7. 2022Improved Algorithms for Neural Active Learningcited by 11
  8. 2021Hitting the Target: Stopping Active Learning at the Cost-Based Optimumcited by 9
  9. 2025Capturing Individual Human Preferences with Reward Featurescited by 8
  10. 2024Active Preference Learning for Ordering Items In- and Out-of-samplecited by 6
  11. 2021Active learning for imbalanced data under cold startcited by 5
  12. 2024ALPBench: A Benchmark for Active Learning Pipelines on Tabular Datacited by 5
24 more
  1. 2021A Survey on Active Deep Learning: From Model-driven to Data-drivencited by 4
  2. 2023Leveraging Importance Weights in Subset Selectioncited by 4
  3. 2023Fairness-Aware Data Valuation for Supervised Learningcited by 4
  4. 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
  5. 2022Margin-based sampling in high dimensions: When being active is less efficient than staying passivecited by 3
  6. 2021Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomographycited by 2
  7. 2022PyRelationAL: a python library for active learning research and developmentcited by 2
  8. 2022Efficient Classification with Counterfactual Reasoning and Active Learningcited by 2
  9. 2024Efficient Biological Data Acquisition through Inference Set Designcited by 2
  10. 2021Feedback Coding for Active Learningcited by 1
  11. 2022Time-Constrained Learningcited by 1
  12. 2022Active Learning for Computationally Efficient Distribution of Binary Evolution Simulationscited by 1
  13. 2023Does Deep Active Learning Work in the Wild?cited by 1
  14. 2023OpenAL: Evaluation and Interpretation of Active Learning Strategiescited by 1
  15. 2023On the Convergence of Loss and Uncertainty-based Active Learning Algorithmscited by 1
  16. 2026Active Evaluation of General Agents: Problem Definition and Comparison of Baseline Algorithmscited by 1
  17. 2021Active Learning for Argument Mining: A Practical Approachcited by 0
  18. 2021A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasurescited by 0
  19. 2022Improving the quality control of seismic data through active learningcited by 0
  20. 2023Active learning with biased non-response to label requestscited by 0
  21. 2024Quantifying Local Model Validity using Active Learningcited by 0
  22. 2025Near-Polynomially Competitive Active Logistic Regressioncited by 0
  23. 2025Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Gridcited by 0
  24. 2026FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systemscited by 0