# Context-Lumpable Bandits

For stochastic contextual bandits whose contexts fall into unknown blocks with identical per-arm mean rewards, online expected regret now matches the known lower bound up to logarithmic factors under arbitrary context distributions and without separation between blocks, achieved without recovering the unknown partition.

For stochastic contextual bandits whose contexts fall into unknown blocks with identical per-arm mean rewards, online expected regret now matches the known lower bound up to logarithmic factors under arbitrary context distributions and without separation between blocks, achieved without recovering the unknown partition.