← 研踪 PaperTrailCVF · CVPR 2026:cvf:3dca8930b75e1b34ca5f41eaf4870464 · 记录 #1
Spectral Conformal Risk Control: Distribution-Free Tail Guarantees via Bayesian Quadrature
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:23
摘要
Modern vision systems are deployed in settings where occasional catastrophic failures matter more than average accuracy--for example in medical imaging, autonomous driving, and safety monitoring. While conformal prediction gives distribution-free uncertainty guarantees, most existing methods only control mean error and are hard to tune toward rare but high-cost mistakes. We propose Bayesian-Quadrature Spectral Risk Control (BQ-SRC), a general framework for controlling tail-focused risks (such as conditional value at risk (CVaR)-style objectives) in a distribution-free way. BQ-SRC views conformal prediction through a Bayesian-quadrature lens and replaces mean-risk control with a flexible family of risk-averse criteria, while keeping the same black-box access to a trained model. A binomial testing scheme reduces the Monte Carlo conservatism of prior approaches, leading to tighter sets without sacrificing guarantees. We evaluate BQ-SRC across diverse vision tasks, including synthetic regression, closed-set and zero-shot image classification, multilabel classification, and semantic segmentation. Across these settings, BQ-SRC consistently maintains finite-sample risk guarantees and often yields smaller or otherwise more informative prediction sets than existing conformal and risk-controlling baselines, sometimes trading a modest amount of efficiency for stronger tail-risk control. The code will be publicly available at https://github.com/MohammadMahdiKazemi/BQ_SRC.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
Spectral Conformal Risk Control: Distribution-Free Tail Guarantees via Bayesian Quadrature
Modern vision systems are deployed in settings where occasional catastrophic failures matter more than average accuracy--for example in medical imaging, autonomous driving, and safety monitoring. While conformal prediction gives distribution-free uncertainty guarantees, most existing methods only control mean error and are hard to tune toward rare but high-cost mistakes. We propose Bayesian-Quadrature Spectral Risk Control (BQ-SRC), a general framework for controlling tail-focused risks (such as conditional value at risk (CVaR)-style objectives) in a distribution-free way. BQ-SRC views conformal prediction through a Bayesian-quadrature lens and replaces mean-risk control with a flexible family of risk-averse criteria, while keeping the same black-box access to a trained model. A binomial testing scheme reduces the Monte Carlo conservatism of prior approaches, leading to tighter sets without sacrificing guarantees. We evaluate BQ-SRC across diverse vision tasks, including synthetic regression, closed-set and zero-shot image classification, multilabel classification, and semantic segmentation. Across these settings, BQ-SRC consistently maintains finite-sample risk guarantees and often yields smaller or otherwise more informative prediction sets than existing conformal and risk-controlling baselines, sometimes trading a modest amount of efficiency for stronger tail-risk control. The code will be publicly available at https://github.com/MohammadMahdiKazemi/BQ_SRC.
查看此版本解读 →查看此版本原文 ↗