Oracle inequalities for multi-fold cross validation

Vaart, Aad W. van der, Dudoit, Sandrine, Laan, Mark J. van der·2006·Statistics & Decisions
DOI 10.1524/stnd.2006.24.3.351

Cited by (136)

Items 101–136 of 136

  1. Two Become One: Improving the Targeting of Conditional Cash Transfers with a Predictive Model of School Dropout
    Cristian Crespo · 2019 · EconomiA
  2. Classification using Ensemble Learning under Weighted Misclassification Loss
    Yizhen Xu, Tao Liu, M. Daniels, R. Kantor, A. Mwangi, J. Hogan · 2018 · Statistics in Medicine
  3. Adaptive sequential surveillance with network and temporal dependence.
    I. Malenica, J. Coyle, M. V. van der Laan, Maya L. Petersen · 2024 · Biometrics
  4. Super Ensemble Learning Using the Highly-Adaptive-Lasso
    Zeyi Wang, Wenxin Zhang, Brian S. Caffo, Martin Lindquist, M. V. van der Laan · 2023
  5. SpiderLearner: An ensemble approach to Gaussian graphical model estimation
    K. Shutta, L. Balzer, D. Scholtens, R. Balasubramanian · 2021 · bioRxiv
  6. Nonparametric targeted Bayesian estimation of class proportions in unlabeled data.
    Iv'an D'iaz, Oleksander Savenkov, H. Kamel · 2019 · Biostatistics
  7. Contextual ranking by passive safety of generational classes of light vehicles
    Zaïd Ouni, Christophe Denis, C. Chauvel, A. Chambaz · 2018 · Journal of the Royal Statistical Society Series C (Applied Statistics)
  8. Sequential Super Learning
    Sherri Rose, Mark J. van der Laan · 2018 · Springer series in statistics
  9. Conformalized Super Learner
    Zhan Wu, Fabrizio Leisen, M. Luque-fernandez, F. Rubio · 2026 · arXiv.org
  10. Extension of coupling via the Projection of Optimal Transport
    Jakwang Kim, Young-Heon Kim, Chanmoo Park · 2026
  11. Prediction Intervals and Group Variable Importance for Classification Models in University Enrollment Yield
    Xiaohui Yin, Ying-Fa Xie, Jun Yan, Siyan Wang, Pang-Yu Liu, Jeffrey Gagnon, E. Thompson, Lawrence Walsh, Nathan Fuerst, Ming-Hui Chen · 2026 · Journal of Data Science
  12. A Density Ratio Super Learner
    Wencheng Wu, David Benkeser · 2024 · arXiv.org
  13. The joint survival super learner: A super learner for right‐censored data
    Anders Munch, T. A. Gerds · 2024 · Statistica neerlandica (Print)
  14. Quantile Super Learning for independent and online settings with application to solar power forecasting
    Herbert Susmann, Antoine Chambaz · 2025 · Computational Statistics & Data Analysis
  15. concrete: Targeted Estimation of Survival and Competing Risks in Continuous Time
    David Chen, H. Rytgaard, E. Fong, J. Tarp, Maya L. Petersen, M. V. van der Laan, T. Gerds · 2023
  16. Personalized dynamic super learning: an application in predicting hemodiafiltration convection volumes
    Arthur Chatton, M. Bally, Renée Lévesque, Ivana Malenica, R. W. Platt, M. Schnitzer · 2023 · Journal of the Royal Statistical Society, Series C: Applied Statistics
  17. Guidance on individualized treatment rule estimation in high dimensions
    P. Boileau, Ning Leng, S. Dudoit · 2023 · The International Journal of Biostatistics
  18. Methodological considerations for novel approaches to covariate‐adjusted indirect treatment comparisons
    Antonio Remiro-Azócar, A. Heath, G. Baio · 2023 · Research Synthesis Methods
  19. Distribution-Free Deviation Bounds and the Role of Domain Knowledge in Learning via Model Selection with Cross-Validation Risk Estimation
    Diego Marcondes, C. Peixoto · 2023 · Machine-mediated learning
  20. Authors' reply to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
    S. Vansteelandt, O. Dukes · 2022 · Journal of The Royal Statistical Society Series B-statistical Methodology
  21. Personalized Online Machine Learning
    I. Malenica, R. Phillips, R. Pirracchio, A. Chambaz, A. Hubbard, M. V. van der Laan · 2021 · arXiv.org
  22. Learning the hypotheses space from data through a U-curve algorithm: a statistically consistent complexity regularizer for Model Selection
    Diego Marcondes, Adilson Simonis, J. Barrera · 2021 · arXiv.org
  23. Conditional Super Learner
    Gilmer Valdes, Y. Interian, E. Gennatas, M. V. van der Laan · 2019 · arXiv.org
  24. Targeted learning: From MLE to TMLE
    M. V. van der Laan · 2014
  25. Collaborative representations for targeted causal inference under outcome selection
    Johan de Aguas · 2026
  26. Soft Learning
    Mohammed Aledhari, Ali Aledhari, Fatimah Aledhari, Mohamed Rahouti · 2026 · arXiv.org
  27. Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences
    M. Kitch, Nihar B. Shah · 2026 · arXiv.org
  28. A Riesz Representer Perspective on Targeted Learning
    Salvador V. Balkus, C. Testa, N. Hejazi · 2026
  29. Nonparametric assessment of regimen response curve estimators
    Cuong Pham, Benjamin R. Baer, Ashkan Ertefaie · 2025 · Biometrics
  30. Multi-task Highly Adaptive Lasso
    I. Malenica, R. Phillips, D. Lazzareschi, J. Coyle, R. Pirracchio, M. V. van der Laan · 2023 · arXiv.org
  31. Statistical Inference for Machine Learning Inverse Probability Weighting with Survival Outcomes
    Iván Díaz · 2017
  32. Optimal oracle inequalities for model selection
    Charles Mitchell, S. A. van de Geer · 2008
  33. Online Super Learning
    M. V. van der Laan, David C. Benkeser · 2018
  34. Research Questions in Data Science
    S. Rose, M. V. van der Laan · 2018 · Springer series in statistics
  35. Data-Adaptive Estimation in Cluster Randomized Trials
    L. Balzer, M. V. van der Laan, M. Petersen · 2018 · Springer series in statistics
  36. Oracle inequalities using truncation and cross-validation
    Charles Mitchell · 2010