Research map / Probabilistic and scientific modeling / Molecules and proteins
Classification with Valid and Adaptive Coverage
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use generalized inverse quantile conformity scores with split-conformal calibration instead of homogeneous classification scores | Classification prediction sets · Simulated multinomial data | worst-slice conditional coverage | Helped |
| Use generalized inverse quantile conformity scores with CV+ or jackknife+ calibration instead of homogeneous classification scores | Classification prediction sets · Mice Protein Expression | conditional coverage | Helped |
| Use generalized inverse quantile conformity scores with SC or CV+ instead of HCC, CQC, or CQC-RF | Classification prediction sets · MNIST with SVC or NNet | conditional coverage | Helped |
| Use CV+ instead of SC | Proposed classification method · Mice Protein Expression | prediction-set size | Helped |
| Use jackknife+ instead of CV+ or SC | Proposed classification method · Simulated data | prediction-set size | Helped |
| Use the proposed method with RFC probabilities rather than SVC probabilities | Proposed classification method · MNIST | conditional coverage | Hurt |
| Use the proposed method with RFC rather than NNet probabilities | Proposed classification method · MNIST | prediction-set size | Hurt |
| Replace the quantile neural network estimator with quantile random forests (ablation) | CQC · Mice Protein Expression and MNIST | prediction-set size | Helped |
| Use NNet instead of RFC as the base probability model (ablation) | Proposed method · MNIST | prediction-set size | Helped |
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- Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage (ICML 2025 · similarity 0.88)
- Valid Selection among Conformal Sets (NeurIPS 2025 · similarity 0.87)
- Backward Conformal Prediction (NeurIPS 2025 · similarity 0.87)
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