A new evaluation framework shows that blood glucose forecasting models with high overall accuracy often fail at timely hypoglycemia detection in high-risk periods and at predicting effects of changed insulin doses.
Econometrica: journal of the Econometric Society , pages=
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
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.
Establishes a uniform Bahadur representation for sieve M-estimators under temporal dependence and constructs valid simultaneous confidence regions using Gaussian approximation and self-convolved bootstrap.
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.
Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.
citing papers explorer
-
From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
A new evaluation framework shows that blood glucose forecasting models with high overall accuracy often fail at timely hypoglycemia detection in high-risk periods and at predicting effects of changed insulin doses.
-
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.
-
Simultaneous Inference for Nonlinear Time Series, a Sieve M-regression Approach
Establishes a uniform Bahadur representation for sieve M-estimators under temporal dependence and constructs valid simultaneous confidence regions using Gaussian approximation and self-convolved bootstrap.
-
A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.
-
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.
- Estimation and Inference for the $\tau$-Quantile of Individual Heterogeneous Coefficient