Research map / Learning theory and algorithms / Prediction and decision losses
Computing Full Conformal Prediction Set with Approximate Homotopy
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use approximate homotopy instead of split conformal | Ridge logcosh regression · Boston | Length: 2.271 -> 1.998 (ε=1e-2) | Helped |
| Use approximate homotopy instead of split conformal | Ridge Linex regression · Diabetes | Length: 2.447 -> 2.231 (ε=1e-2) | Helped |
| Tighten optimization tolerance from 1e-2 to 1e-8 (ablation) | Approximate homotopy · Boston | Length: 1.998 -> 1.981 | Helped |
| Tighten optimization tolerance from 1e-2 to 1e-8 (ablation) | Approximate homotopy · Boston | Time (s): 0.073 -> 36.977 | Hurt |
| Tighten optimization tolerance from 1e-2 to 1e-8 (ablation) | Approximate homotopy · Diabetes | Length: 2.231 -> 2.199 | Helped |
| Tighten optimization tolerance from 1e-2 to 1e-8 (ablation) | Approximate homotopy · Diabetes | Time (s): 0.050 -> 20.712 | Hurt |
| Use approximate homotopy at the reported tolerances (ablation) | Ridge logcosh regression · Boston | Coverage: 0.92 at 1e-2, 1e-4, 1e-6, and 1e-8 | No clear effect |
| Use approximate homotopy at the reported tolerances (ablation) | Ridge Linex regression · Diabetes | Coverage: 0.91 at 1e-2, 1e-4, 1e-6, and 1e-8 | No clear effect |
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