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University of Bristol
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Intrinsic randomness in Gaussian process sampling can yield strong differential privacy guarantees, challenging conventional noise addition methods.
NNGP/GPnn isn't just fast, it's theoretically sound: this paper proves it achieves optimal convergence rates and robustness, making it a principled alternative to full GPs for massive datasets.
