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Finite-sample guarantees reveal how localized conformal prediction can significantly reduce miscalibration while preserving coverage.
Unifying diverse mathematical frameworks reveals critical insights into convergence and performance guarantees for reinforcement learning algorithms.
Ditch reward models: Nash Mirror Prox achieves fast, stable convergence to a Nash equilibrium directly from human preferences, sidestepping the limitations of traditional RLHF.
