aiqbench analysis
SPACR introduces a novel training framework that integrates uncertainty quantification directly into the loss function, eliminating the need for post-hoc calibration or costly retraining to achieve valid prediction intervals. By jointly optimizing accuracy and efficiency within a single pass, this method offers tighter bounds and improved coverage-efficiency trade-offs across diverse datasets compared to standard conformal approaches. This advancement addresses a key bottleneck in deploying robust machine learning models where computational resources and inference speed are critical constraints.
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
arXiv:2606.10734v2 Announce Type: replace Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model training with the conformal goal of producing efficient (i.e., narrow) intervals. We propose SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss. SPACR jointly optimizes accuracy, efficiency, and validity without batch-splitting or a predefined confidence level during training. As a result, a single SPACR model yields valid prediction intervals at multiple confidence levels during inference, avoiding the costly retraining required by methods like Directly Optimized Inductive Conformal Regression (DOICR). Experiments on diverse tabular and image datasets show that SPACR consistently gives tighter intervals and better coverage-efficiency trade-offs compared to standard CP and DOICR, while significantly reducing computational costs relative to retraining-dependent baselines.
Abstract:Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model training with the conformal goal of producing efficient (i.e., narrow) intervals. We propose SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss. SPACR jointly optimizes accuracy, efficiency, and validity without batch-splitting or a predefined confidence level during training. As a result, a single SPACR model yields valid prediction intervals at multiple confidence levels during inference, avoiding the costly retraining required by methods like Directly Optimized Inductive Conformal Regression (DOICR). Experiments on diverse tabular and image datasets show that SPACR consistently gives tighter intervals and better coverage-efficiency trade-offs compared to standard CP and DOICR, while significantly reducing computational costs relative to retraining-dependent baselines.
Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
arXiv:2606.10734 [cs.LG]
(or arXiv:2606.10734v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.10734 Focus to learn more arXiv-issued DOI via DataCite
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