Research map / Probabilistic and scientific modeling / Bayesian optimization
Pareto Frontier Learning with Expensive Correlated Objectives
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model correlations between objectives (CEIPV with correlated GP) instead of independent GPs | multi-objective Bayesian optimization (EIPV acquisition) · synthetic and real multi-objective benchmarks | Pareto hypervolume | Helped |
| increase correlation strength between objectives (higher |rho|) | CEIPV vs. other methods on synthetic SLF problems · synthetic SLF, rho in {-0.5,0,0.25,0.75} | Pareto hypervolume gain of CEIPV over others | Helped |
| correlated GP model (CEIPV) at zero correlation | CEIPV vs IEIPV · synthetic SLF, rho=0 | Pareto hypervolume | No clear effect |
| NSGA-II instead of EIPV-based Bayesian optimization | multi-objective benchmarks · oka2, vlmop3, dtlz1a, boston, rocket, llvm | Pareto hypervolume | Hurt |
| independent GP objectives (IEIPV) on LLVM task | IEIPV · llvm | Pareto hypervolume | Hurt |
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