Reproduce “Fast Conformal Prediction using Conditional Interquantile Intervals” on OpenResearch
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About this paper
Naixin Guo, Rui Luo, Zhixin Zhou
Conformal Interquantile Regression (CIR and CIR+) provides a fast and robust approach for generating prediction intervals in regression, ensuring finite-sample coverage while adapting to skewed data distributions. These methods produce intervals nearly as narrow as computationally intensive alternatives but with significantly improved speed, making uncertainty quantification more efficient.
Key insights
- Directly utilizing conditional interquantile intervals, rather than explicit density histograms, provides an efficient pathway to adapt to skewed data distributions for prediction intervals.
- Refining conformity scores by incorporating fractional interval contributions allows for finer-grained calibration, leading to slightly narrower prediction intervals (CIR+).
- The methods maintain finite-sample marginal coverage guarantees, upholding the core statistical validity of conformal prediction under data exchangeability.
Summary by alphaXiv.
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