Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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Pith papers citing it
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2026 8representative citing papers
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.
A layered differentiable optimization approach combines conformal prediction for risk-aware ellipsoids with control barrier functions and quadratic programming for safe robot navigation under sensor uncertainty.
citing papers explorer
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Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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Conditional Predictive Inference for General Structured Data with Group Symmetries
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
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A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
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Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.
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Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.
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Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction
A layered differentiable optimization approach combines conformal prediction for risk-aware ellipsoids with control barrier functions and quadratic programming for safe robot navigation under sensor uncertainty.
- A Unified Theory of Conditional Coverage in Conformal Prediction with Applications