# Bayesian Active Learning for Censored Regression

arXiv: https://arxiv.org/abs/2402.11973 · HTML: https://mlsanity.com/paper/2402.11973/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Deep Active Learning for Named Entity Recognition](https://mlsanity.com/paper/1707.05928/) (2017)
- [Gone Fishing: Neural Active Learning with Fisher Embeddings](https://mlsanity.com/paper/2106.09675/) (2021)
- [Do Bayesian Neural Networks Need To Be Fully Stochastic?](https://mlsanity.com/paper/2211.06291/) (2022)
- [Prediction-Oriented Bayesian Active Learning](https://mlsanity.com/paper/2304.08151/) (2023)
- [PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection](https://mlsanity.com/paper/2103.00128/) (2021)
- [X-CAL: Explicit Calibration for Survival Analysis](https://mlsanity.com/paper/2101.05346/) (2021)
- [Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data](https://mlsanity.com/paper/2111.02275/) (2021)
- [Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities](https://mlsanity.com/paper/2208.00549/) (2022)
- [Bayesian Active Learning with Fully Bayesian Gaussian Processes](https://mlsanity.com/paper/2205.10186/) (2022)
- [An Effective Meaningful Way to Evaluate Survival Models](https://mlsanity.com/paper/2306.01196/) (2023)
- [A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer](https://mlsanity.com/paper/1804.03280/) (2018)
- [Black-Box Batch Active Learning for Regression](https://mlsanity.com/paper/2302.08981/) (2023)
- [Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes](https://mlsanity.com/paper/2001.07402/) (2020)
- [Modeling Censored Mobility Demand through Quantile Regression Neural Networks](https://mlsanity.com/paper/2104.01214/) (2021)
- [Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand](https://mlsanity.com/paper/2301.06418/) (2023)

## Built on by (most cited first, 2 of 2)

- [Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels](https://mlsanity.com/paper/2409.04313/) (2024)
- [Budget-constrained Active Learning to Effectively De-censor Survival Data](https://mlsanity.com/paper/2510.12144/) (2025)

## 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.