mlsanityData to 7 Oct 2026

Targeted Active Learning for Bayesian Decision-Making

arXiv 2106.04193 ↗First posted 8 Jun 2021stat.ML cs.AI cs.LGcited by 12

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Abstract (arXiv)

Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way. However, maximizing the accuracy is not the end goal when the results are used for decision-making, for example in personalized medicine or economics. We argue that when acquiring samples sequentially, separating learning and decision-making is sub-optimal, and we introduce an active learning strategy which takes the down-the-line decision problem into account. Specifically, we introduce a novel active learning criterion which maximizes the expected information gain on the posterior distribution of the optimal decision. We compare our targeted active learning strategy to existing alternatives on both simulated and real data, and show improved performance in decision-making accuracy.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 50 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

20 of 50 references.

References · 20

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  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
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  11. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
  12. 2020"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairnesscited by 56
8 more
  1. 2018Loss-Calibrated Approximate Inference in Bayesian Neural Networkscited by 18
  2. 2019Active Learning for Decision-Making from Imbalanced Observational Datacited by 16
  3. 2015Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyondcited by 15
  4. 2019Variational Bayesian Decision-making for Continuous Utilitiescited by 15
  5. 2017Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitationcited by 8
  6. 2018Active Learning with Logged Datacited by 7
  7. 2019Understanding Goal-Oriented Active Learning via Influence Functionscited by 6
  8. 2016Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Predictioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 2; 2026: 5.

12 papers.

  1. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  2. 2022Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluationcited by 26
  3. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  4. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  5. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  6. 2025BioBO: Biology-informed Bayesian Optimization for Perturbation Designcited by 2
  7. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  8. 2026Loss-Driven Bayesian Active Learningcited by 1
  9. 2022Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluationcited by 0
  10. 2026Robust Bayesian Decision Making under Adversarial Uncertaintycited by 0
  11. 2026FleetSieve: Decision-Critical Profiling for SLO-Aware LLM Fleet Configurationcited by 0
  12. 2026Beyond "AI Helps Humans": Decision-Targeted Evaluation Design for Human-Agent Teams in the Agentic Eracited by 0