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School of Electrical and Computer Engineering, Purdue University
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The JMDP framework reveals that optimal control can be achieved even in environments with multiple competing actions, fundamentally shifting how we approach decision-making under uncertainty.
Pseudo-calibration can provably salvage conformal prediction's coverage guarantees under distribution shift, offering a way to maintain reliability when your training data doesn't match the real world.