Active Learning & Adaptive Sampling Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Label efficiency matters. This list surveys uncertainty, diversity, and disagreement-based strategies for choosing what to label next.

Active Learning & Adaptive Sampling: 10 key papers

1. A Survey of Deep Active Learning
   Ren et al. arXiv 2020.
2. BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
   Kirsch et al. arXiv 2019.
3. Deep Bayesian Active Learning with Image Data
   Gal et al. arXiv 2017.
4. Active Learning for Convolutional Neural Networks: A Core-Set Approach
   Sener and Savarese. arXiv 2017.
5. Learning Loss for Active Learning
   Yoo and Kweon. arXiv 2019.
6. Bayesian Active Learning for Classification and Preference Learning
   Houlsby et al. arXiv 2011.
7. Variational Adversarial Active Learning
   Sinha et al. arXiv 2019.
8. Active Learning for Deep Object Detection
   Brust et al. arXiv 2018.
9. Deep Active Learning for Named Entity Recognition
   Shen et al. arXiv 2017.
10. Discriminative Active Learning
    Gissin and Shalev-Shwartz. arXiv 2019.

Related reading lists

• Adaptive Sensing and Active Learning
• Bayesian RL & Thompson Sampling
• Bayesian Deep Learning
• Anomaly & Out-of-Distribution Detection
• Bayesian Optimization & Global Optimization
• Preference Learning & Learning from Comparisons
• Out-of-Distribution & Robustness
• Federated & Distributed Learning