Contextual Slate GLM Bandits with Limited Adaptivity

We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity.

At each round, the learner is presented with $N$ sets of items, where each item is represented by a $d$-dimensional feature vector.

The learner then constructs a slate by selecting one item per set; the resulting slate yields a scalar reward sampled from a Generalized Linear Model (GLM).

We propose algorithms under two limited-adaptivity settings: (a) Batched and (b) Rarely-Switching.

For the batched setting, we introduce B-SlateGLinCB, which partitions the time horizon into $\mathcal{O}(\log\log T)$ batches such that each batch's policy relies only on data from previous batches.

For the rarely-switching setting, we propose RS-SlateGLinCB, which adaptively performs only $\mathcal{O}(Nd\log T)$ parameter updates.

Under a diversity assumption on the item sequences, we prove that B-SlateGLinCB and RS-SlateGLinCB achieve regret bounds of $\mathcal{O}(Nd^{3/2}\sqrt{T})$ and $\mathcal{O}(Nd\sqrt{T})$, respectively.

Notably, both bounds are independent of the non-linearity parameter $\kappa$ that is typically found to scale the regret of GLM bandit algorithms.

Our algorithms are computationally efficient, requiring only $\text{poly}(N)$ time per round despite $2^{\Omega(N)}$ possible slates.

Simulations show our algorithms outperform existing baselines with limited adaptivity and remain competitive with Slate-GLM-OFU, a fully adaptive state-of-the-art algorithm.

Notably, a slightly modified B-SlateGLinCB empirically matches this baseline.

Finally, we demonstrate strong performance in a practical in-context example selection task for language models.