Formulating ensemble selection as mutual-information maximization reveals an information-theoretic error floor from model correlation and yields a greedy algorithm that outperforms baselines under fixed query budgets.
For discrete-time finite-horizon mean-field games with state-independent transitions and weakly monotone rewards, anchored proximal gradient descent computes mean-field equilibria via monotone inclusions over occupation measures at an O(1/√T) rate without regularization or uniqueness.