CD-split and HPD-split: efficient conformal regions in high dimensions
Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. Although the literature has mostly focused on prediction intervals, more general regions can often better represent uncertainty. For instance, a bimodal target is better represented by the union of two intervals. Such prediction regions are obtained by CD-split , which combines the split method and a data-driven partition of the feature space which scales to high dimensions. CD-split however contains many tuning parameters, and their role is not clear. In this paper, we provide new insights on CD-split by exploring its theoretical properties. In particular, we show that CD-split converges asymptotically to the oracle highest predictive density set and satisfies local and asymptotic conditional validity. We also present simulations that show how to tune CD-split. Finally, we introduce HPD-split, a variation of CD-split that requires less tuning, and show that it shares the same theoretical guarantees as CD-split. In a wide variety of our simulations, CD-split and HPD-split have better conditional coverage and yield smaller prediction regions than other methods.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionPrediction IntervalsVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
MD-split+: Practical Local Conformal Inference in High Dimensions
Quantifying uncertainty in model predictions is a common goal for practitioners seeking more than just point predictions. One tool for uncertainty quantification that requires minimal assumptions is conformal inference, …
Conformal PredictionDensity EstimationOpen-Ended Question AnsweringPrediction+3Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction
Modern black-box predictive models are often accompanied by weak performance guarantees that only hold asymptotically in the size of the dataset or require strong parametric assumptions. In response to this, split confor…
Conformal PredictionPredictionquantile regressionFlexible distribution-free conditional predictive bands using density estimators
Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditional coverage, that is, coverage for eve…
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning
Bayesian deep learning and conformal prediction are two methods that have been used to convey uncertainty and increase safety in machine learning systems. We focus on combining Bayesian deep learning with split conformal…
Conformal PredictionDeep Learningimage-classificationImage Classification+2On Optimal Data Splitting for Split Conformal Prediction
Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample covera…