Transductive Conformal Inference With Adaptive Scores: Numerical Bounds and Templates by@transduction
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Transductive Conformal Inference With Adaptive Scores: Numerical Bounds and Templates
by Transduction University PapersFebruary 28th, 2024
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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
B Numerical bounds and templates
The bound proposed in Theorem 2.3 are explicit and elegant, but can be conservative in some cases and we develop here the numerical approach mentioned in Remark 2.5.
Hence, let us consider
L O A D I N G
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