Contextual bandits with concave rewards, and an application to fair ranking
This addresses fairness in recommendation systems by enabling provable regret guarantees for multi-objective bandit problems, though it builds incrementally on prior constrained optimization methods.
The paper tackles the problem of contextual bandits with concave rewards (CBCR) by presenting the first algorithm with provably vanishing regret without restrictions on policy space, achieving this through a novel reduction to scalar-reward bandit problems. It applies this to fair ranking, resulting in the first algorithm with regret guarantees for contextual combinatorial bandits with fairness of exposure.
We consider Contextual Bandits with Concave Rewards (CBCR), a multi-objective bandit problem where the desired trade-off between the rewards is defined by a known concave objective function, and the reward vector depends on an observed stochastic context. We present the first algorithm with provably vanishing regret for CBCR without restrictions on the policy space, whereas prior works were restricted to finite policy spaces or tabular representations. Our solution is based on a geometric interpretation of CBCR algorithms as optimization algorithms over the convex set of expected rewards spanned by all stochastic policies. Building on Frank-Wolfe analyses in constrained convex optimization, we derive a novel reduction from the CBCR regret to the regret of a scalar-reward bandit problem. We illustrate how to apply the reduction off-the-shelf to obtain algorithms for CBCR with both linear and general reward functions, in the case of non-combinatorial actions. Motivated by fairness in recommendation, we describe a special case of CBCR with rankings and fairness-aware objectives, leading to the first algorithm with regret guarantees for contextual combinatorial bandits with fairness of exposure.