Research map / Probabilistic and scientific modeling / Bayesian optimization
Joint Entropy Search for Multi-Objective Bayesian Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use JES to select evaluation points | Multi-objective Bayesian optimization · Four synthetic and real-world benchmarks | HV discrepancy | Helped |
| Use JES to select evaluation points | Multi-objective Bayesian optimization · Four synthetic and real-world benchmarks | weighted HV discrepancy | Helped |
| Use different conditional entropy estimates (JES-MC, JES-LB, or JES-LB2) (ablation) | JES · Benchmarks in the conditional-entropy comparison | HV discrepancy | No clear effect |
| Use the lower-bound entropy estimate instead of a more expensive entropy approximation (ablation) | JES · Benchmarks in the conditional-entropy comparison | wall time | Helped |
| Restrict recommendations to previously sampled locations (ablation) | Information-theoretic acquisition methods · Querying high-performing points | HV discrepancy | Mixed |
| Use an epsilon-greedy strategy that occasionally selects a posterior-mean-greedy point | Information-theoretic acquisition methods · Querying high-performing points | quality of sampled points | Helped |
| Use a crude single-box approximation of the dominated region (ablation) | MESMO / PFES · Multi-objective optimization | HV discrepancy | Hurt |
| Evaluate performance with generalized hypervolume across different objective-space regions | Multi-objective Bayesian optimization algorithms · ZDT2 and a bi-objective example | local Pareto-front assessment | Helped |
Closest papers by meaning
- A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization (ICML 2025 · similarity 0.91)
- Batched Energy-Entropy acquisition for Bayesian Optimization (NeurIPS 2024 · similarity 0.89)
- Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization (NeurIPS 2026 · similarity 0.88)
- SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization (NeurIPS 2026 · similarity 0.88)
- Pareto-frontier Entropy Search with Variational Lower Bound Maximization (ICML 2025 · similarity 0.88)
- BOtied: Multi-objective Bayesian optimization with tied multivariate ranks (ICML 2024 · similarity 0.88)
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