Bandit Pareto Set Identification in a Multi-Output Linear Model
TL;DR: We study the identification of the Pareto set with bandit feedback and observable features in both fixed-confidence and fixed-budget settings
Abstract: We study the Pareto Set Identification (PSI) problem in a structured multi-output linear bandit model. In this setting each arm is associated a feature vector belonging to $\mathbb{R}^h$ and its mean vector in $\mathbb{R}^d$ linearly depends on this feature vector through a common unknown matrix $\Theta \in \mathbb{R}^{h \times d}$. The goal is to identify the set of non-dominated arms by adaptively collecting samples from the arms. We introduce and analyze the first optimal design-based algorithms for PSI, providing nearly optimal guarantees in both the fixed-budget and the fixed-confidence settings. Notably, we show that the difficulty of these tasks mainly depends on the sub-optimality gaps of $h$ arms only.
Our theoretical results are supported by an extensive benchmark on synthetic and real-world datasets.
Submission Number: 412
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