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Differential privacy in language tasks is surprisingly cheap: approximate DP is free, and pure DP only reduces performance by a factor of $\min\{1,\varepsilon\}$.
By intelligently fusing Wiener Chaos Expansion with Neural Operators, this new method cracks the notoriously difficult problem of simulating singular stochastic PDEs without relying on computationally expensive renormalization techniques.
