Chuan Guo is a computer scientist and coauthor of On Calibration of Modern Neural Networks by Chuan Guo and others (2017), which studies whether neural-network confidence agrees with observed correctness. Its experiments and post-processing comparisons helped make probability calibration a concrete evaluation question alongside classification accuracy. The paper marks its first three authors as equal contributors; the work should not be attributed solely to the first-listed author.[ref]
On Calibration of Modern Neural Networks by Chuan Guo and others. First-page byline, equal-contribution note and abstract
The distinction can be illustrated without reproducing an experiment. Suppose a synthetic classifier makes predictions, of which are correct, and reports confidence for every selected class. Its accuracy is , while its mean confidence is . Changing only the reported confidence to leaves the predictions and accuracy unchanged but removes this group's mean confidence gap. Aggregate agreement still does not prove calibration within every subgroup.
Temperature scaling, examined in the paper, makes a related distinction operational by fitting one positive scalar to adjust probabilities while preserving the selected class within each example. The mathematical transformation and its measured limitations belong to temperature scaling; they do not imply that every classifier can be made perfectly calibrated with one number.[ref]
On Calibration of Modern Neural Networks by Chuan Guo and others. §4.2, Eq. (9); §5, calibration comparisons
Homepage: sites.google.com/view/chuanguo . The page lists the calibration paper, distinguishing this author from other researchers with the same name.
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