Covariance-adapting algorithm for semi-bandits with application to sparse rewards
We investigate stochastic combinatorial semi-bandits, where the entire joint distribution of outcomes impacts the complexity of the problem instance (unlike in the standard bandits). Typical distributions considered depend on specific parameter values, whose prior knowledge is required in theory but quite difficult to estimate in practice; an example is the commonly assumed sub-Gaussian family. We alleviate this issue by instead considering a new general family of sub-exponential distributions, which contains bounded and Gaussian ones. We prove a new lower bound on the expected regret on this family, that is parameterized by the unknown covariance matrix of outcomes, a tighter quantity th...
| Authors | Pierre Perrault, Vianney Perchet, Michal Valko |
|---|---|
| Primary category | stat.ML |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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