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Privacy odometers and filters: Pay-as-you-go composition
Ryan M Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan · 2016
Earlier work this paper cites.

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Gaussian differential privacy
Original
Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2019
Cited alongside, same era.

Then

Individual privacy accounting with gaussian differential privacy
Original
Antti Koskela, Marlon Tobaben, and Antti Honkela · 2022
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2022

Fully Adaptive Composition for Gaussian Differential Privacy

Smith, Adam, Thakurta, Abhradeep

Understand

We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a fully adaptive analyst.

Such an analyst selects mechanisms (to be run on a sensitive data set) and their privacy budgets adaptively, that is, based on the answers from other mechanisms run previously on the same data set.
In the language of Rogers, Roth, Ullman and Vadhan, this gives a filter for GDP with the same parameters as for nonadaptive composition.