A metric to improve the robustness of conformal predictors in the presence of error bars

Andrea Murari, Saeed Talebzadeh, Jesús Vega, Emmanuele Peluso, Michela Gelfusa, Michele Lungaroni, Pasqualino Gaudio

Research output: Contribution to conferencePaper

Abstract

Conformal predictors, currently applied to many problems in various fields determine precise levels of confidence in new predictions on the basis only of the information present in the past data, without making recourse to any assumptions except that the examples are generated independently from the same probability distribution. In this paper, the robustness of their results is assessed for the cases in which the data are affected by error bars. This is the situation typical of the physical sciences, whose data are often the results of complex measurement procedures, unavoidably affected by noise. Assuming the noise presents a normal distribution, the Geodesic Distance on Gaussian Manifolds provides a statistical principled and quite effective method to handle the uncertainty in the data. A series of numerical tests prove that adopting this metric in conformal predictors improves significantly their performance, compared to the Euclidean distance, even for relatively low levels of noise.
Original languageEnglish
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016 - Madrid, Spain
Duration: 1 Jan 2016 → …

Conference

Conference5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016
CountrySpain
CityMadrid
Period1/1/16 → …

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Murari, A., Talebzadeh, S., Vega, J., Peluso, E., Gelfusa, M., Lungaroni, M., & Gaudio, P. (2016). A metric to improve the robustness of conformal predictors in the presence of error bars. Paper presented at 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016, Madrid, Spain. https://doi.org/10.1007/978-3-319-33395-3_8