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pro vyhledávání: '"Sivakumar, Satchit"'
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densitie
Externí odkaz:
http://arxiv.org/abs/2406.19566
Privacy is a central challenge for systems that learn from sensitive data sets, especially when a system's outputs must be continuously updated to reflect changing data. We consider the achievable error for differentially private continual release of
Externí odkaz:
http://arxiv.org/abs/2306.06723
Autor:
Bun, Mark, Gaboardi, Marco, Hopkins, Max, Impagliazzo, Russell, Lei, Rex, Pitassi, Toniann, Sivakumar, Satchit, Sorrell, Jessica
The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More precisely, a replicable algorithm gives the same output with high probabi
Externí odkaz:
http://arxiv.org/abs/2303.12921
We initiate an investigation of private sampling from distributions. Given a dataset with $n$ independent observations from an unknown distribution $P$, a sampling algorithm must output a single observation from a distribution that is close in total
Externí odkaz:
http://arxiv.org/abs/2211.08193
We study the accuracy of differentially private mechanisms in the continual release model. A continual release mechanism receives a sensitive dataset as a stream of $T$ inputs and produces, after receiving each input, an accurate output on the obtain
Externí odkaz:
http://arxiv.org/abs/2112.00828
We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning. We apply this transformation to a recently proposed binary private PAC learner to obtain a private multiclass learner with sample complexi
Externí odkaz:
http://arxiv.org/abs/2107.10870