Reproduce “Top-label calibration and multiclass-to-binary reductions” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Chirag Gupta, Aaditya Ramdas
Researchers at Carnegie Mellon University introduce "top-label calibration" to provide a more interpretable measure for multiclass probability predictions and present a Multiclass-to-Binary (M2B) reduction framework. Their proposed methods, TL-HB and CW-HB, based on histogram binning, demonstrate improved performance in reducing Expected Calibration Error and Maximum Calibration Error across various benchmarks and offer distribution-free theoretical guarantees.
Key insights
- Confidence calibration can mask severe, class-specific miscalibration, making its interpretation for critical decision-making contexts problematic; top-label calibration offers a more reliable and actionable alternative.
- Complex multiclass calibration problems can be effectively decomposed and solved by applying robust binary calibrators within a unifying Multiclass-to-Binary reduction framework.
- The common practice of normalizing probabilities in class-wise histogram binning (N-HB) is detrimental to calibration performance; skipping this step, as in CW-HB, yields superior results.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.