We consider the problem of minimizing regret in an $N$ agent heterogeneous stochastic linear bandits framework, where the agents (users) are similar but not all identical. We model user heterogeneity using two popularly used ideas in practice; (i) A clustering framework where users are partitioned into groups with users in the same group being identical to each other, but different across groups, and (ii) a personalization framework where no two users are necessarily identical, but a users parameters are close to that of the population average. In the clustered users setup, we propose a novel algorithm, based on successive refinement of cluster identities and regret minimization. We show that, for any agent, the regret scales as $mathcal{O}(sqrt{T/N})$, if the agent is in a `well separated cluster, or scales as $mathcal{O}(T^{frac{1}{2} + varepsilon}/(N)^{frac{1}{2} -varepsilon})$ if its cluster is not well separated, where $varepsilon$ is positive and arbitrarily close to $0$. Our algorithm is adaptive to the cluster separation, and is parameter free -- it does not need to know the number of clusters, separation and cluster size, yet the regret guarantee adapts to the inherent complexity. In the personalization framework, we introduce a natural algorithm where, the personal bandit instances are initialized with the estimates of the global average model. We show that, an agent $i$ whose parameter deviates from the population average by $epsilon_i$, attains a regret scaling of $widetilde{O}(epsilon_isqrt{T})$. This demonstrates that if the user representations are close (small $epsilon_i)$, the resulting regret is low, and vice-versa. The results are empirically validated and we observe superior performance of our adaptive algorithms over non-adaptive baselines.
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