mlsanityData to 14 Oct 2026

NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification

arXiv 2505.11054 ↗First posted 16 May 2025cs.LG stat.MLcited by 5

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Abstract (arXiv)

We introduce NeuralSurv, the first deep survival model to incorporate Bayesian uncertainty quantification. Our non-parametric, architecture-agnostic framework captures time-varying covariate-risk relationships in continuous time via a novel two-stage data-augmentation scheme, for which we establish theoretical guarantees. For efficient posterior inference, we introduce a mean-field variational algorithm with coordinate-ascent updates that scale linearly in model size. By locally linearizing the Bayesian neural network, we obtain full conjugacy and derive all coordinate updates in closed form. In experiments, NeuralSurv delivers superior calibration compared to state-of-the-art deep survival models, while matching or exceeding their discriminative performance across both synthetic benchmarks and real-world datasets. Our results demonstrate the value of Bayesian principles in data-scarce regimes by enhancing model calibration and providing robust, well-calibrated uncertainty estimates for the survival function.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

17 of 48 references.

References · 17

  1. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  2. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
  3. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
  4. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  5. 2017Machine Learning for Survival Analysis: A Surveycited by 112
  6. 2023Deep Learning for Survival Analysis: A Reviewcited by 75
  7. 2018A Scalable Discrete-Time Survival Model for Neural Networkscited by 72
  8. 2019Continuous and Discrete-Time Survival Prediction with Neural Networkscited by 70
  9. 2018Effective Ways to Build and Evaluate Individual Survival Distributionscited by 64
  10. 2015On Sparse variational methods and the Kullback-Leibler divergence between stochastic processescited by 57
  11. 2021Survival Regression with Proper Scoring Rules and Monotonic Neural Networkscited by 42
  12. 2018Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processescited by 17
5 more
  1. 2016Gaussian Processes for Survival Analysiscited by 14
  2. 2023Proper Scoring Rules for Survival Analysiscited by 9
  3. 2023DySurv: dynamic deep learning model for survival analysis with conditional variational inferencecited by 9
  4. 2019Structured Variational Inference in Continuous Cox Process Modelscited by 8
  5. 2024TorchSurv: A Lightweight Package for Deep Survival Analysiscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discoverycited by 23
  2. 2025Position: Stop Chasing the C-index when Evaluating Survival Analysis Modelscited by 7
  3. 2026SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inferencecited by 3
  4. 2026Good Rankings, Wrong Probabilities: A Calibration Audit of Multimodal Cancer Survival Modelscited by 1
  5. 2026Uncertainty quantification for trustworthy deep learning: Methods and measurescited by 0