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Bayesian Batch Active Learning as Sparse Subset Approximation

arXiv 1908.02144 ↗First posted 6 Aug 2019stat.ML cs.LGcited by 68

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Abstract (arXiv)

Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change. In this paper, we introduce a novel Bayesian batch active learning approach that mitigates these issues. Our approach is motivated by approximating the complete data posterior of the model parameters. While naive batch construction methods result in correlated queries, our algorithm produces diverse batches that enable efficient active learning at scale. We derive interpretable closed-form solutions akin to existing active learning procedures for linear models, and generalize to arbitrary models using random projections. We demonstrate the benefits of our approach on several large-scale regression and classification tasks.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Aug 2019.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 12; 2022: 15; 2023: 19; 2024: 11; 2025: 6; 2026: 4.

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  1. 2023Modern Bayesian Experimental Designcited by 125
  2. 2022A Comparative Survey of Deep Active Learningcited by 89
  3. 2023Prediction-Oriented Bayesian Active Learningcited by 62
  4. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 49
  5. 2021Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learningcited by 36
  6. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26
  7. 2023Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessmentcited by 26
  8. 2021Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learningcited by 25
  9. 2021Diversity Enhanced Active Learning with Strictly Proper Scoring Rulescited by 22
  10. 2023Data-centric Graph Learning: A Surveycited by 22
  11. 2024Active Learning for Neural PDE Solverscited by 22
  12. 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombinationcited by 20
38 more
  1. 2021Active Learning for Deep Visual Trackingcited by 19
  2. 2023Black-Box Batch Active Learning for Regressioncited by 12
  3. 2024Making Better Use of Unlabelled Data in Bayesian Active Learningcited by 10
  4. 2021Bayesian Active Learning by Disagreements: A Geometric Perspectivecited by 9
  5. 2023Deep Anomaly Detection under Labeling Budget Constraintscited by 9
  6. 2023Agile Modeling: From Concept to Classifier in Minutescited by 8
  7. 2023Accelerating Batch Active Learning Using Continual Learning Techniquescited by 8
  8. 2021Visual Transformer for Task-aware Active Learningcited by 7
  9. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  10. 2024Querying Easily Flip-flopped Samples for Deep Active Learningcited by 7
  11. 2023Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approachcited by 6
  12. 2023Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learningcited by 6
  13. 2024AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasetscited by 5
  14. 2021A Survey on Active Deep Learning: From Model-driven to Data-drivencited by 4
  15. 2021One Backward from Ten Forward, Subsampling for Large-Scale Deep Learningcited by 4
  16. 2022Black-box Coreset Variational Inferencecited by 4
  17. 2022Feeding What You Need by Understanding What You Learnedcited by 3
  18. 2023AdaSelection: Accelerating Deep Learning Training through Data Subsamplingcited by 3
  19. 2024Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimationcited by 3
  20. 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspectivecited by 3
  21. 2019A New Framework for Query Efficient Active Imitation Learningcited by 2
  22. 2021Marginally calibrated response distributions for end-to-end learning in autonomous drivingcited by 2
  23. 2022Active metric learning and classification using similarity queriescited by 2
  24. 2022PyRelationAL: a python library for active learning research and developmentcited by 2
  25. 2022Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applicationscited by 2
  26. 2021Feedback Coding for Active Learningcited by 1
  27. 2022Black-box Generalization of Machine Teachingcited by 1
  28. 2022Batch Active Learning from the Perspective of Sparse Approximationcited by 1
  29. 2023Learning Objective-Specific Active Learning Strategies with Attentive Neural Processescited by 1
  30. 2023Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detectioncited by 1
  31. 2024STONE: A Submodular Optimization Framework for Active 3D Object Detectioncited by 1
  32. 2021A Unified Batch Selection Policy for Active Metric Learningcited by 0
  33. 2021Understanding Uncertainty in Bayesian Deep Learningcited by 0
  34. 2022Using Sum-Product Networks to Assess Uncertainty in Deep Active Learningcited by 0
  35. 2022Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantificationcited by 0
  36. 2022Active Learning for Regression with Aggregated Outputscited by 0
  37. 2022Frugal Reinforcement-based Active Learningcited by 0
  38. 2022Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detectioncited by 0

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