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On the Calibration of Aggregated Conformal Predictors
Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT. (CSL@BS)
Swetox, Karolinska Institutet.
Dept. of Computer Science and Informatics, Stockholm University.
Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
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2017 (Engelska)Ingår i: Proceedings of Machine Learning Research, 2017Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Conformal prediction is a learning framework that produces models that associate witheach of their predictions a measure of statistically valid confidence. These models are typi-cally constructed on top of traditional machine learning algorithms. An important result ofconformal prediction theory is that the models produced are provably valid under relativelyweak assumptions—in particular, their validity is independent of the specific underlyinglearning algorithm on which they are based. Since validity is automatic, much research onconformal predictors has been focused on improving their informational and computationalefficiency. As part of the efforts in constructing efficient conformal predictors, aggregatedconformal predictors were developed, drawing inspiration from the field of classification andregression ensembles. Unlike early definitions of conformal prediction procedures, the va-lidity of aggregated conformal predictors is not fully understood—while it has been shownthat they might attain empirical exact validity under certain circumstances, their theo-retical validity is conditional on additional assumptions that require further clarification.In this paper, we show why validity is not automatic for aggregated conformal predictors,and provide a revised definition of aggregated conformal predictors that gains approximatevalidity conditional on properties of the underlying learning algorithm.

Ort, förlag, år, upplaga, sidor
2017.
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Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:hb:diva-13636OAI: oai:DiVA.org:hb-13636DiVA, id: diva2:1181777
Konferens
Conformal and Probabilistic Prediction and Applications, Stockholm Sweden 13-16 June, 2017
Tillgänglig från: 2018-02-09 Skapad: 2018-02-09 Senast uppdaterad: 2020-01-29Bibliografiskt granskad

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