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A simple framework for constructing differentially private confidence regions by adding noise only to the final resampling quantile instead of privatizing the estimator computed on each resample, avoiding the computational and privacy costs of privatizing many intermediate resample statistics.
We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final resampling quantile instead of privatizing the estimator computed on each resample. The cost of privacy of our procedure is only logarithmic in the number of resamples $...
Modern generative models are increasingly trained using model-generated signals, creating both opportunities for self-improvement and risks of collapse. We study optimal self-distillation (SD) for rectified flow (RF): given a suboptimal teacher velocity field, can a student trained on a mixture of true RF velocities an...
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