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Poster
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Youngseog Chung · Willie Neiswanger · Ian Char · Jeff Schneider

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @
in Poster Session 6 »

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 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, select an explicit balance 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.

Keywords: Machine Learning

Author Information

Youngseog Chung (Carnegie Mellon University)
Willie Neiswanger (Stanford University)
Ian Char (Carnegie Mellon University)
Jeff Schneider (CMU)

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