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Optimal sequential hypothesis testing with Markovian data is now possible, thanks to a new lower bound that incorporates both stationary distribution and transition structure, leading to an asymptotically optimal test.
Knowing the sequence of model updates lets you pinpoint when a specific data point was added to the training set, making membership inference attacks far more potent than previously thought.
