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19 April 2024 |
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Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification | | Youngseog Chung
; Willie Neiswanger
; Ian Char
; Jeff Schneider
; | | Date: |
19 Nov 2020 | | Abstract: | Among the many ways of quantifying uncertainty in a regression setting,
specifying the full quantile function is attractive, as quantiles are amenable
to interpretation and evaluation. A model that predicts the true conditional
quantiles for each input, at all quantile levels, presents a correct and
efficient representation of the underlying uncertainty. To achieve this, many
current quantile-based methods focus on optimizing the so-called pinball loss.
However, this loss restricts the scope of applicable regression models, limits
the ability to target many desirable properties (e.g. calibration, sharpness,
centered intervals), and may produce poor conditional quantiles. In this work,
we develop new quantile methods that address these shortcomings. In particular,
we propose methods that can apply to any class of regression model, allow for
selecting a Pareto-optimal trade-off between calibration and sharpness,
optimize for calibration of centered intervals, and produce more accurate
conditional quantiles. We provide a thorough experimental evaluation of our
methods, which includes a high dimensional uncertainty quantification task in
nuclear fusion. | | Source: | arXiv, 2011.09588 | | Services: | Forum | Review | PDF | Favorites | |
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