Research map / Probabilistic and scientific modeling / Bayesian optimization
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MF-HV-KG (multi-fidelity HV-KG) instead of MOMF and other non-MF baselines | multi-fidelity MOBO problems · Park, MF Branin Currin, Ranking Policy Optimization, Plasma Laser Acceleration | log hypervolume regret | Helped |
| Use decoupled HV-KG instead of decoupled PFES, JES-LB2, and decoupled Sobol (competitive decoupling) | decoupled MOBO problems with competitive decoupling · ZDT2, DTLZ2, Vehicle Design, NAS (CD) | log hypervolume regret | Mixed |
| Use decoupled HV-KG instead of baselines (non-competitive decoupling) | decoupled MOBO problems with non-competitive decoupling · ZDT2, DTLZ2, Vehicle Design, NAS (NCD) | log hypervolume regret | Helped |
| Replace nested stochastic optimization with sample average approximation (deterministic one-shot optimization) for HV-KG (ablation) | HV-KG acquisition optimization · acquisition optimization wall time and acquisition value | acquisition value and wall time | Helped |
| Random (quasi-random) decoupled asynchronous candidate generation vs standard qNEHVI BO on NAS | decoupled asynchronous evaluation · NAS | log regret | Helped |
| Exploit decoupled evaluations with HV-KG versus entropy-based decoupled methods (PFES, JES) | decoupled MOBO · standard decoupled benchmarks | sensitivity to cost function | Helped |
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