Download Conformal Prediction for Reliable Machine Learning Theory, by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk PDF

By Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

The conformal predictions framework is a contemporary improvement in desktop studying that could affiliate a competent degree of self assurance with a prediction in any real-world development popularity software, together with risk-sensitive purposes reminiscent of scientific analysis, face reputation, and monetary probability prediction. Conformal Predictions for trustworthy computer studying idea, variations and Applications captures the elemental concept of the framework, demonstrates tips to use it on real-world difficulties, and offers a number of diversifications, together with energetic studying, swap detection, and anomaly detection. As practitioners and researchers worldwide observe and adapt the framework, this edited quantity brings jointly those our bodies of labor, offering a springboard for additional study in addition to a instruction manual for software in real-world difficulties.

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Extra resources for Conformal Prediction for Reliable Machine Learning Theory, Adaptations and Applications

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12)) and set + (z 1 , . . 14) where z (·) refers to the reordering of the z i such that pˆl (z (1) ) ≤ · · · ≤ pˆl (z (l) ) and k := (l+1) . The set predictor + is not a conformal predictor but it is guaranteed to satisfy (z 1 , . . , zl ) ⊆ + (z 1 , . . , zl ) (and so is conservatively valid) and Theorem is replaced by + . ’s paper [200] contains more general results. 1 becomes much faster: namely, we can replace the exponent 1/(d + 2) by an exponent as close to 1/2 as we wish. Even faster rates of convergence can be achieved if we replace Assumption 4 by an assumption of a faster change of the density q when moving away from the set {q = t}.

22 23 25 25 27 27 31 34 34 36 38 43 44 44 46 An appealing property of conformal predictors is their automatic validity under the exchangeability assumption: They make an error with probability not exceeding the prespecified significance level. A major focus of this chapter will be on “conditional” versions of the notion of validity. 7. 8). 5 we will discuss classical tolerance regions, which can be regarded as a special case of conformal predictors and as a generalization of inductive conformal predictors; their importance in the context of this chapter lies in the possibility of extending to them certain properties of conditional validity enjoyed by inductive conformal predictors.

Partial example conditional validity (including object conditional validity) will be considered in detail in the next section. • PAC-type object conditional validity: The conditional probability of error given the test object does not exceed a prespecified level with a high probability. In fact, we do not discuss PAC-type object conditional validity in this book. 4. • Asymptotic object conditional validity, where E can be any property of the test example but the conditional probability of error is required to converge to the significance level only as the sample size goes to infinity.

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