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Found 4 papers, 0 papers with code
Strong posterior contraction rates via Wasserstein dynamics
In Bayesian statistics, posterior contraction rates (PCRs) quantify the speed at which the posterior distribution concentrates on arbitrarily small neighborhoods of a true model, in a suitable way, as the sample size goes to infinity.
The power of private likelihood-ratio tests for goodness-of-fit in frequency tables
This is obtained through a Bahadur-Rao large deviation expansion for the power of the private LR test, bringing out a critical quantity, as a function of the sample size, the dimension of the table and $(\varepsilon,\delta)$, that determines a loss in the power of the test.
Learning-augmented count-min sketches via Bayesian nonparametrics
Under this more general framework, we apply the arguments of the ``Bayesian" proof of the CMS-DP, suitably adapted to the PYP prior, in order to compute the posterior distribution of a point query, given the hashed data.
A Bayesian nonparametric approach to count-min sketch under power-law data streams
The count-min sketch (CMS) is a randomized data structure that provides estimates of tokens' frequencies in a large data stream using a compressed representation of the data by random hashing.
