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Ditching pessimism unlocks a quadratically faster, $\widetilde{\mathcal{O}}(1/n)$ sample complexity for offline learning in KL-regularized games.
Autonomous vehicles can now better identify the unexpected, thanks to a new method that boosts out-of-distribution detection by up to 20% without retraining.
