Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration

摘要

We introduce the first variance-aware algorithms for contextual dueling bandits that leverage shallow exploration strategies with neural networks for nonlinear utility approximation. A key theoretical challenge is the absence of a closed-form estimator, which led prior work to require an extremely large network width m (i.e., m = Ω(T^14)). We address this constraint with a novel analytical approach that combines iterative self-improvement with spectral analysis. Our analysis significantly reduces the network width requirement to m = Ω(T^6), and shows that our algorithms achieve a sublinear regret of 𝒪(d√(∑_t=1^Tσ_t^2) + √(dT)) under both UCB and TS frameworks. Empirical results show that the proposed algorithms are not only computationally efficient and exhibit sublinear regret in practical settings, but also achieve state-of-the-art performance on both synthetic and real-world tasks.