Research map / Learning theory and algorithms / Prediction and decision losses
Formalizing Preferences Over Runtime Distributions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Choose a different utility function for evaluating runtime distributions | minisat configurations · CNFuzzDD instances | expected utility | Mixed |
| Rank solvers using different utility functions | 2021 International SAT Competition Parallel Track solvers · 2021 International SAT Competition Parallel Track | ranking | Mixed |
| Estimate expected utility from capped runtime samples rather than estimating mean runtime | arbitrary algorithms · runtime samples | estimation accuracy | Helped |
| Use maximum-entropy captime distributions when the captime distribution is underspecified | runtime-distribution scoring · underspecified captime distributions | scoring-function specification | Helped |
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