Reverse Conformal Approach for On-line Experimental Design

Ilia Nouretdinov
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:185-192, 2017.

Abstract

Conformal prediction is a recently developed framework of confident machine learning with guaranteed validity properties for prediction sets. In this work we study its usage in reversed version of the traditional machine learning problem: prediction of objects which can have a given label, instead of usual prediction of labels by objects. It is meant that the label reflect some desired property of the object. For this kind of task, the conformal prediction framework can provide a prediction set that is a set of objects that are likely to have the label. Based on this, we create an on-line protocol of experimental design. It includes a choice criterion based on conformal output, and elements of transfer learning in order to keep the validity properties in on-line regime.

Cite this Paper

BibTeX
@InProceedings{pmlr-v60-nouretdinov17a, title = {Reverse Conformal Approach for On-line Experimental Design}, author = {Nouretdinov, Ilia}, booktitle = {Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {185--192}, year = {2017}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Papadopoulos, Harris}, volume = {60}, series = {Proceedings of Machine Learning Research}, month = {13--16 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v60/nouretdinov17a/nouretdinov17a.pdf}, url = {https://proceedings.mlr.press/v60/nouretdinov17a.html}, abstract = {Conformal prediction is a recently developed framework of confident machine learning with guaranteed validity properties for prediction sets. In this work we study its usage in reversed version of the traditional machine learning problem: prediction of objects which can have a given label, instead of usual prediction of labels by objects. It is meant that the label reflect some desired property of the object. For this kind of task, the conformal prediction framework can provide a prediction set that is a set of objects that are likely to have the label. Based on this, we create an on-line protocol of experimental design. It includes a choice criterion based on conformal output, and elements of transfer learning in order to keep the validity properties in on-line regime.} }
Endnote
%0 Conference Paper %T Reverse Conformal Approach for On-line Experimental Design %A Ilia Nouretdinov %B Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2017 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Harris Papadopoulos %F pmlr-v60-nouretdinov17a %I PMLR %P 185--192 %U https://proceedings.mlr.press/v60/nouretdinov17a.html %V 60 %X Conformal prediction is a recently developed framework of confident machine learning with guaranteed validity properties for prediction sets. In this work we study its usage in reversed version of the traditional machine learning problem: prediction of objects which can have a given label, instead of usual prediction of labels by objects. It is meant that the label reflect some desired property of the object. For this kind of task, the conformal prediction framework can provide a prediction set that is a set of objects that are likely to have the label. Based on this, we create an on-line protocol of experimental design. It includes a choice criterion based on conformal output, and elements of transfer learning in order to keep the validity properties in on-line regime.
APA
Nouretdinov, I.. (2017). Reverse Conformal Approach for On-line Experimental Design. Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 60:185-192 Available from https://proceedings.mlr.press/v60/nouretdinov17a.html.

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