Few-for-Many Best-Arm Identification in Multi-Objective Bandits
Modeling each option as an arm in a multi-objective bandit, we introduce few-for-many best-arm identification, which seeks a small set of arms minimizing the worst weighted degradation from the objective-wise optima, rather than the best arm for every objective.
ProofPaper ↗
Key points
- Real-world systems often serve many objectives but can deploy only a few options, whose performance must be learned from noisy feedback.
- We consider both known objective groups and latent groups specified only by a representative budget, and propose GFM-Elim and AFM-Elim with instance-dependent sample-complexity guarantees.
- For Gaussian rewards, we further derive a lower bound for the latent-group setting and show that it is asymptotically attainable with high probability by an oracle-based Track-and-Stop procedure.
- Our results show that sharing observations across objectives and ignoring irrelevant objective-wise distinctions can substantially reduce the sampling cost, as confirmed by experiments on synthetic and real-world tasks.
Sources (1)
- [1]Few-for-Many Best-Arm Identification in Multi-Objective BanditsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 02:18 PM
Modeling each option as an arm in a multi-objective bandit, we introduce few-for-many best-arm identification, which seeks a small set of arms minimizing the worst weighted degradation from the objective-wise optima, rather than the best arm for every objective.
Real-world systems often serve many objectives but can deploy only a few options, whose performance must be learned from noisy feedback.
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