TL;DR —
Conformal inference is a fundamental and versatile tool that provides distribution-free guarantees for many machine learning tasks.
This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ulysse Gazin, Universit´e Paris Cit´e and Sorbonne Universit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation,
(2) Gilles Blanchard, Universit´e Paris Saclay, Institut Math´ematique d’Orsay,
(3) Etienne Roquain, Sorbonne Universit´e and Universit´e Paris Cit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation.
Table of Links
- Abstract & Introduction
- Main results
- Application to prediction intervals
- Application to novelty detection
- Conclusion, Acknowledgements and References
- Appendix A: Exact formulas for Pn,m
- Appendix B: Numerical bounds and templates
- Appendix C: Proof
- Appendix D: Explicit control of (16) for α=0
- Appendix E: Proof of Corollary 4.1
- Appendix F: The Simes inequality
- Appendix G: Uniform FDP bound for AdaDetect
- Appendix H: Additional experiments
G Uniform FDP bound for AdaDetect
It is proved there to control the false discovery rate (FDR), defined as the mean of the FDP:
Applying Corollary 24, we obtain on the top of the in-expectation guarantee (49) the following uniform FDP bound for ADα: with probability at least 1 − δ, we have
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machine-learning|conformal-inference|what-is-multiple-testing|what-is-false-discovery-rate|what-is-uniform-error-control|machine-learning-research|ml-research-papers|adaptive-scores
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