Contextual bandits are a class of online learning algorithms that, given an input context, select an action (arm) and observe a reward for that action, using the reward as a learning signal to maximize cumulative reward over a sequence of inputs. They are a standard approach for recommendation and ranking problems and have been adapted for specialized settings including privacy-preserving multi-party learning, robustness to adversarial action-poisoning, and, as demonstrated by ReFuzz, for selecting and mutating tests in processor hardware fuzzing.

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