Parameter-Free Dynamic Regret for Unconstrained Linear Bandits
We study dynamic regret minimization in unconstrained adversarial linear bandit problems. In this setting, a learner must minimize the cumulative loss relative to an arbitrary sequence of comparators $\boldsymbol{u}_1,\ldots,\boldsymbol{u}_T$ in $\mathbb{R}^d$, but receives only point-evaluation feedback on each round. We provide a simple approach to combining the guarantees of several bandit algorithms, allowing us to optimally adapt to the number of switches $S_T = \sum_t\mathbb{I}\{\boldsymbol{u}_t \neq \boldsymbol{u}_{t-1}\}$ of an arbitrary comparator sequence. In particular, we provide the first algorithm for linear bandits achieving the optimal regret guarantee of order $\mathcal{O...
| Authors | Alberto Rumi, Andrew Jacobsen, Nicolò Cesa-Bianchi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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