mlsanityData to 14 Oct 2026

Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift

arXiv 2306.16406 ↗First posted 28 Jun 2023stat.ME math.ST stat.MLcited by 5

Not on the map. Why?

Abstract (arXiv)

Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such dataset shift conditions are known as domain adaptation or transfer learning. Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population. In this paper, we study the general problem of efficiently estimating target population risk under various dataset shift conditions, leveraging semiparametric efficiency theory. We consider a general class of dataset shift conditions, which includes three popular conditions -- covariate, label and concept shift -- as special cases. We allow for partially non-overlapping support between the source and target populations. We develop efficient and multiply robust estimators along with a straightforward specification test of these dataset shift conditions. We also derive efficiency bounds for two other dataset shift conditions, posterior drift and location-scale shift. Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 41 of its 134 references that are in the map. First posted on arXiv 28 Jun 2023.

Builds on

41 of 134 references.

References · 41

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2019Conformal Prediction Under Covariate Shiftcited by 519
  3. 2022Conformal prediction beyond exchangeabilitycited by 355
  4. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  5. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
  6. 2018Dimensionality-Driven Learning with Noisy Labelscited by 173
  7. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 151
  8. 2018An introduction to domain adaptation and transfer learningcited by 112
  9. 2013A Conformal Prediction Approach to Explore Functional Datacited by 86
  10. 2017Double/Debiased/Neyman Machine Learning of Treatment Effectscited by 76
  11. 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 70
  12. 2018Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Datacited by 66
29 more
  1. 2019Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifiercited by 65
  2. 2022Optimally tackling covariate shift in RKHS-based nonparametric regressioncited by 54
  3. 2021PAC Prediction Sets Under Covariate Shiftcited by 45
  4. 2022Doubly Robust Calibration of Prediction Sets under Covariate Shiftcited by 40
  5. 2016Semi-supervised Inference: General Theory and Estimation of Meanscited by 38
  6. 2020Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Controlcited by 38
  7. 2017Efficient and Adaptive Linear Regression in Semi-Supervised Settingscited by 29
  8. 2018Predicting with Proxies: Transfer Learning in High Dimensioncited by 24
  9. 2018A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptationcited by 23
  10. 2022Prediction Sets Adaptive to Unknown Covariate Shiftcited by 22
  11. 2020PAC Confidence Predictions for Deep Neural Network Classifierscited by 21
  12. 2017Fisher consistency for prior probability shiftcited by 20
  13. 2020A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sumcited by 19
  14. 2022Robust angle-based transfer learning in high dimensionscited by 16
  15. 2022Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Featurescited by 13
  16. 2022Semi-supervised Triply Robust Inductive Transfer Learningcited by 12
  17. 2023The Fundamental Limits of Structure-Agnostic Functional Estimationcited by 12
  18. 2020Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Modelscited by 11
  19. 2020Assumption-lean inference for generalised linear model parameterscited by 9
  20. 2022A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimalitycited by 9
  21. 2019High-dimensional semi-supervised learning: in search for optimal inference of the meancited by 8
  22. 2020Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learningcited by 7
  23. 2022A unified framework for dataset shift diagnosticscited by 7
  24. 2021On Doubly Robust Inference for Double Machine Learning in Semiparametric Regressioncited by 6
  25. 2021Transfer Learning in High-dimensional Semi-parametric Graphical Models with Application to Brain Connectivity Analysiscited by 5
  26. 2021Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlapcited by 4
  27. 2021Efficient Estimation Under Data Fusioncited by 4
  28. 2018Doubly Robust Regression Analysis for Data Fusioncited by 1
  29. 2023Mixed Semi-Supervised Generalized-Linear-Regression with Applications to Deep-Learning and Interpolatorscited by 1

Built on by

Cited by year (2026 to 14 Oct)

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

5 papers.

  1. 2025General Synthetic-Powered Inferencecited by 6
  2. 2024A hierarchical decomposition for explaining ML performance discrepanciescited by 5
  3. 2025A Conditional Distribution Equality Testing Framework using Deep Generative Learningcited by 1
  4. 2024DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimationcited by 0
  5. 2026Inferential Evaluation of Surrogate-Derived Models under Covariate Shiftcited by 0