Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
The s-value: evaluating stability with respect to distributional shifts
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use partial transfer with covariates identified as unstable | ATE estimation on the DJW/DJWC split · Lalonde DJW/DJWC | ATE estimation error | Helped |
| Use full transfer with all covariates instead of partial transfer | ATE estimation on the DJW/DJWC split · Lalonde DJW/DJWC | ATE estimation error | No clear effect |
| Use partial transfer with covariates identified as unstable | Regression coefficient estimation · Wine Quality | estimation error | Helped |
| Use full transfer with all covariates instead of partial transfer | Regression coefficient estimation · Wine Quality | estimation error | No clear effect |
| Apply transfer at α=0.01 for the density coefficient | Regression coefficient estimation · Wine Quality | estimation error | No clear effect |
| Use directional s-values to identify unstable covariates before collecting target-distribution data | Two-stage parameter transfer · NSW and Wine Quality | estimation error | Helped |
Closest papers by meaning
- Stability Evaluation through Distributional Perturbation Analysis (ICML 2024 · similarity 0.88)
- Not all distributional shifts are equal: Fine-grained robust conformal inference (ICML 2024 · similarity 0.85)
- Quantifying Uncertainty in the Presence of Distribution Shifts (NeurIPS 2025 · similarity 0.85)
- Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations (NeurIPS 2025 · similarity 0.85)
- Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference (ICML 2024 · similarity 0.84)
- Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution (ICML 2024 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.