mlsanityData to 7 Oct 2026

Bayesian Active Learning for Censored Regression

arXiv 2402.11973 ↗First posted 19 Feb 2024cs.LG stat.MLcited by 2

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Abstract (arXiv)

Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is commonly done by maximising the Bayesian Active Learning by Disagreement (BALD) acquisitions function. However, we highlight that it is challenging to estimate BALD when the new data points are subject to censorship, where only clipped values of the targets are observed. To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censored regression (C-BALD). We propose a novel modelling approach to estimate the C-BALD objective and use it for active learning in the censored setting. Across a wide range of datasets and models, we demonstrate that C-BALD outperforms other Bayesian active learning methods in censored regression.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

22 of 49 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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10 more
  1. 2021X-CAL: Explicit Calibration for Survival Analysiscited by 30
  2. 2022Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantitiescited by 28
  3. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 27
  4. 2022Bayesian Active Learning with Fully Bayesian Gaussian Processescited by 26
  5. 2023An Effective Meaningful Way to Evaluate Survival Modelscited by 15
  6. 2018A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancercited by 12
  7. 2023Black-Box Batch Active Learning for Regressioncited by 12
  8. 2020Estimating Latent Demand of Shared Mobility through Censored Gaussian Processescited by 7
  9. 2021Modeling Censored Mobility Demand through Quantile Regression Neural Networkscited by 5
  10. 2023Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demandcited by 3

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labelscited by 5
  2. 2025Budget-constrained Active Learning to Effectively De-censor Survival Datacited by 0