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Classical CUSUM relies on the log-likelihood ratio of the underlying distributions, which cannot generally be computed from finite pre- and post-change samples alone. We propose diffusion-integrated score CUSUM (DI-SCUSUM), a training-free detector. We add Gaussian noise to the samples to form two smooth density estima...
It is proved that in-context learning extends further to data generation: frozen transformers can simulate iterative generative samplers from in-context samples, and it is proved that transformers can approximate an energy-based sampler.
The upper bound analyzes an unbiased falling-factorial estimator based on $\alpha$-way collisions, while a hidden-heavy-coordinate construction gives the matching lower bound and shows that the factor $\alpha$ is unavoidable.
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