Managing Self-Learning Experts under Per-Round Budget Constraints
M-LCB is a new UCB-type algorithm for managing K self-learning experts with a limited per-round budget M. It allocates training updates using lower confidence bounds to ensure all experts get data, and selects the expert to deploy using upper confidence bounds to keep immediate loss low. This yields anytime regret bounds scaling with individual expert regrets.