Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization

arXiv:2609.31940 · cs.LG, math.OC, stat.ML · Submitted 2026-09-25 · Read on arXiv

cs.LG, math.OC, stat.ML

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Benchmark — 49× in this paper · explained in 2 episodes
• Acquisition Function — 48× in this paper · explained in 1 episode
• Bayesian Optimization — 25× in this paper · explained in 3 episodes
• Benchmarking — 8× in this paper · explained in 3 episodes
• Regret — 7× in this paper · explained in 3 episodes
• Multi-objective Optimization — 5× in this paper · explained in 4 episodes
• Expected Improvement — 4× in this paper
• Joint Entropy Search — 4× in this paper
• Adaptive Selection — 3× in this paper · explained in 1 episode
• Information Gain — 3× in this paper · explained in 2 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Bayesian Optimization (BO) — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Efficient Global Optimization — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Multi-Objective Joint Entropy Search — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Modular Components — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Upper-Confidence Bound — 1× in this paper

Sources

• Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
• Predictive Entropy Search for Multi-objective Bayesian Optimization
• Multi-objective Bayesian Optimization using Pareto-frontier Entropy
• Joint Entropy Search for Multi-objective Bayesian Optimization
• Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

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