New PDF release: Conformal Prediction for Reliable Machine Learning. Theory,

By Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

ISBN-10: 0123985374

ISBN-13: 9780123985378

The conformal predictions framework is a contemporary improvement in laptop studying that may affiliate a competent degree of self assurance with a prediction in any real-world trend reputation program, together with risk-sensitive functions comparable to scientific analysis, face reputation, and fiscal danger prediction. Conformal Predictions for trustworthy computing device studying: concept, diversifications and Applications captures the fundamental idea of the framework, demonstrates the way to use it on real-world difficulties, and provides a number of diversifications, together with lively studying, switch detection, and anomaly detection. As practitioners and researchers worldwide observe and adapt the framework, this edited quantity brings jointly those our bodies of labor, supplying a springboard for additional examine in addition to a instruction manual for program in real-world problems.

  • Understand the theoretical foundations of this crucial framework which could offer a competent degree of self assurance with predictions in computing device learning
  • Be in a position to observe this framework to real-world difficulties in several laptop studying settings, together with class, regression, and clustering
  • Learn powerful methods of adapting the framework to more moderen challenge settings, similar to energetic studying, version choice, or switch detection

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Additional info for Conformal Prediction for Reliable Machine Learning. Theory, Adaptations and Applications

Sample text

L+1 is generated arbitrarily, for each category κ the multiset of examples z i with category κi = κ is also generated arbitrarily but the order of these examples is chosen randomly (with each order having the same probability). To see that this assumption (which is an example of an online compression model, discussed in [365], pp. 198–199) can be significantly less restrictive than the exchangeability assumption, consider the following example [294]. ” However, if we define the category of an example to be its label, the exchangeability assumption becomes satisfied or almost satisfied; for example, the instances of the letter “q” might well be exchangeable.

Z m ), zl+1 ) of zl+1 does not exceed for any significance level and any conditional inductive conformal predictor corresponding to K . 2 we saw that the notion of conformal predictor can be modified in a natural way to improve its example conditional validity. In this section we take up 27 28 CHAPTER 2 Beyond the Basic Conformal Prediction Framework training conditional validity. , the probability of the event zl+1 ∈ (z 1 , . . , zl )) conditional on the training set (z 1 , . . , zl ) of a set predictor under the randomness assumption is Q( (z 1 , .

23). 23) implies that is object conditionally valid in an asymptotic sense. For the additional properties of efficiency and validity (on top of what is guaranteed for all conditional conformal predictors) established by Lei and Wasserman for their predictor the following regularity conditions are sufficient: 1. The marginal distribution Q X of Q has a differentiable density that is bounded above and bounded away from 0. 2. The conditional Q-probability distribution Q x of the label y given any object x has a differentiable density qx .

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Conformal Prediction for Reliable Machine Learning. Theory, Adaptations and Applications by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk


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