Provably Sample-Efficient Active Preference Data Collection

Collecting human preference feedback is often expensive, leading recent works to develop algorithms to select them more efficiently. However, these works assume that the underlying reward function is linear, an assumption that does not hold in many real-life applications, e.g., online recommendation. To address this limitation, we propose Neural-ADB, an algorithm based on the neural contextual dueling bandit framework that provides a practical method for collecting human preference feedback when the underlying latent reward function is non-linear. We theoretically show that when preference feedback follows the Bradley-Terry-Luce model, the worst sub-optimality gap of the policy learned by Neural-ADB decreases at a sub-linear rate as the preference dataset increases. Our experimental results on preference datasets further corroborate the effectiveness of Neural-ADB.