mlsanityData to 7 Oct 2026

Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics

arXiv 2211.10508 ↗First posted 18 Nov 2022stat.ML cs.LGcited by 14

Not on the map. Why?

Abstract (arXiv)

We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a user-specified minimum probability). This approach uses a training loss function that does not know any demographic information to treat as sensitive. Despite this, we demonstrate that our proposed approach often scores better on recently established fairness metrics (without a significant drop in prediction accuracy) compared to various baselines, including ones which directly use sensitive demographic information in their training loss. Our code is available at: https://github.com/discovershu/DRO_COX

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 18 Nov 2022.

Builds on

17 of 43 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationcited by 1,232
  3. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  4. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 354
  5. 2018Learning Models with Uniform Performance via Distributionally Robust Optimizationcited by 334
  6. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
  7. 2018An Intersectional Definition of Fairnesscited by 126
  8. 2017Machine Learning for Survival Analysis: A Surveycited by 112
  9. 2020Distributionally Robust Losses for Latent Covariate Mixturescited by 57
  10. 2020Learning by Minimizing the Sum of Ranked Rangecited by 27
  11. 2021TkML-AP: Adversarial Attacks to Top-k Multi-Label Learningcited by 26
  12. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26
5 more
  1. 2021Sum of Ranked Range Loss for Supervised Learningcited by 23
  2. 2022Longitudinal Fairness with Censorshipcited by 21
  3. 2020Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervalscited by 12
  4. 2021Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Modelscited by 5
  5. 2022Flexible Group Fairness Metrics for Survival Analysiscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 5; 2026: 1.

14 papers.

Sort:

By area:

  1. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  2. 2023Improving Fairness in Deepfake Detectioncited by 40
  3. 2024LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Modelscited by 31
  4. 2024Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Imagescited by 20
  5. 2024Robust Light-Weight Facial Affective Behavior Recognition with CLIPcited by 16
  6. 2024Fairness in Survival Analysis with Distributionally Robust Optimizationcited by 13
  7. 2024Robust COVID-19 Detection in CT Images with CLIPcited by 12
  8. 2023Outlier Robust Adversarial Trainingcited by 10
  9. 2025Improving Generalization of Medical Image Registration Foundation Modelcited by 6
  10. 2025Robust AI-Generated Face Detection with Imbalanced Datacited by 5
  11. 2025Robust Fairness Vision-Language Learning for Medical Image Analysiscited by 4
  12. 2025Competing Risks: Impact on Risk Estimation and Algorithmic Fairnesscited by 2
2 more
  1. 2025Distributionally Robust Learning in Survival Analysiscited by 0
  2. 2026Robust Multi-Source Covid-19 Detection in CT Imagescited by 0