Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Conformal Bayesian Computation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply CB conformalization to a misspecified (c = 0.02) Bayesian model | Bayesian lasso regression · Diabetes | coverage: 0.563 -> 0.809 | Helped |
| Apply CB to a Bayesian model with tight prior on residual scale | Bayesian lasso regression · Boston, c = 0.02 | coverage: 0.728 -> 0.799 | Helped |
| Apply CB to a well-specified Bayesian model | Bayesian lasso regression · Diabetes, c = 1 | coverage: 0.806 -> 0.808 | No clear effect |
| Apply CB to an over-covering Bayesian model | Bayesian lasso regression · Boston, c = 1 | interval length: 1.35 -> 1.12 | Helped |
| Apply CB to over-covering Bayesian classifier | Bayesian logistic regression · Breast Cancer | coverage: 0.990 -> 0.812 | Helped |
| Apply CB to Bayesian classifier | Bayesian logistic regression · Parkinson's | set size: 1.31 -> 0.93 | Helped |
| Replace Laplace-prior model with ridge-type prior d = 0.001 (strong shrinkage) (ablation) | Bayesian regression · Diabetes | Bayes coverage: 0.805 -> 0.779 | Hurt |
| Use CB instead of full conformal lasso for speed | Full conformal · Diabetes | run-time (s): 11.529 -> 0.702 | Helped |
| Use CB instead of Bayes in hierarchical misspecified setting | Hierarchical Bayes · Simulated Scenario 2 group 2 | coverage: 0.522 -> 0.812 | Helped |
| Use CB in the heteroscedastic scenario | Hierarchical Bayes · Simulated Scenario 2 group 2 | interval length: 3.61 -> 7.27 | Hurt |
| Use CB in the well-specified hierarchical scenario | Hierarchical Bayes · Simulated Scenario 1 | overall length: 2.77 -> 3.13 | Hurt |
| Use CB on classification, measured by misclassification among singleton sets | Bayesian logistic regression · Breast Cancer | misclassification rate: 0.011 -> 0.002 | Helped |
Closest papers by meaning
- Conformal Prediction as Bayesian Quadrature (ICML 2025 · similarity 0.90)
- CBMA: Improving Conformal Prediction through Bayesian Model Averaging (ICLR 2025 · similarity 0.90)
- Self-Calibrating Conformal Prediction (NeurIPS 2024 · similarity 0.88)
- Probabilistic Conformal Prediction with Approximate Conditional Validity (ICLR 2025 · similarity 0.87)
- Not all distributional shifts are equal: Fine-grained robust conformal inference (ICML 2024 · similarity 0.87)
- Rectifying Conformity Scores for Better Conditional Coverage (ICML 2025 · similarity 0.86)
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