UPS framework uses conformal prediction to calibrate VLM verifiers for choosing between high-confidence action execution, natural language task queries, or policy interventions, then applies residual learning from interventions to continually improve the base policy with minimal feedback.
Springer, 2005
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Differentiable nonconformity scores induce flows that sample conformal prediction set boundaries, and mixing flows across levels produces conformal predictive distributions whose quantiles match the sets.
An online conformal prediction framework for non-exchangeable panel data that forms prediction sets using related units' contemporaneous data with adaptive similarity weights and miscoverage levels to deliver stepwise and long-run coverage guarantees.
Pooled conformal calibration incurs irreducible group-wise coverage distortion scaled by cross-group quantile heterogeneity, with Equalized Coverage and Equalized Set Size in fundamental tension.
OLCP and OLCP-Hedge achieve long-run valid coverage in non-exchangeable online settings with narrower prediction sets by localizing conformal prediction to covariates and selecting bandwidth via online convex optimization.
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
Empirical comparison of deep ensembles and Monte Carlo dropout with GNLL and MSE losses, plus recalibration, shows DE and recalibrated GNLL perform best for predictive robustness and uncertainty calibration in PPG-based BP estimation under domain shift.
citing papers explorer
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When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
UPS framework uses conformal prediction to calibrate VLM verifiers for choosing between high-confidence action execution, natural language task queries, or policy interventions, then applies residual learning from interventions to continually improve the base policy with minimal feedback.
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Flow-Based Conformal Predictive Distributions
Differentiable nonconformity scores induce flows that sample conformal prediction set boundaries, and mixing flows across levels produces conformal predictive distributions whose quantiles match the sets.
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Online Conformal Prediction for Non-Exchangeable Panel Data
An online conformal prediction framework for non-exchangeable panel data that forms prediction sets using related units' contemporaneous data with adaptive similarity weights and miscoverage levels to deliver stepwise and long-run coverage guarantees.
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On the Burden of Achieving Fairness in Conformal Prediction
Pooled conformal calibration incurs irreducible group-wise coverage distortion scaled by cross-group quantile heterogeneity, with Equalized Coverage and Equalized Set Size in fundamental tension.
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Online Localized Conformal Prediction
OLCP and OLCP-Hedge achieve long-run valid coverage in non-exchangeable online settings with narrower prediction sets by localizing conformal prediction to covariates and selecting bandwidth via online convex optimization.
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A Kernel Nonconformity Score for Multivariate Conformal Prediction
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
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Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
Empirical comparison of deep ensembles and Monte Carlo dropout with GNLL and MSE losses, plus recalibration, shows DE and recalibrated GNLL perform best for predictive robustness and uncertainty calibration in PPG-based BP estimation under domain shift.