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Unlock $\sqrt{N}$ regret in offline policy learning, even with complex policy classes, by trading off policy and environment complexity.
Unlock asymptotically normal and semiparametrically efficient estimators in adaptive data collection by using a novel target-specific condition called "directional stability," which is weaker than previous target-agnostic conditions.
