DP-Dueling: Learning from Preference Feedback without Compromising User Privacy

arXiv:2403.15045v1 Announce Type: cross
Abstract: We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy. We consider a general class of utility-based preference matrices for large (potentially unbounded) decision spaces and give the first differentially private dueling bandit algorithm for active learning with user preferences. Our proposed algorithms are computationally efficient with near-optimal performance, both in terms of the private and non-private regret bound. More precisely, …

actions arxiv bandit class comparisons cs.cr cs.lg decision differential privacy feedback general identify large near privacy private problem under user privacy utility