no code implementations • 23 Sep 2020 • Jakub Tětek
We also note that sampling edges from a distribution sufficiently close to uniform is sufficient to be able to simulate sublinear algorithms that use the random edge queries while decreasing the success probability of the algorithm only by $o(1)$.
Data Structures and Algorithms
no code implementations • 12 Nov 2020 • Jakub Tětek, Marek Sklenka, Tomáš Gavenčiak
We show that this result is no longer true for agents with bounded rationality.
no code implementations • 3 Feb 2021 • Kasper Green Larsen, Rasmus Pagh, Jakub Tětek
For $t > 1$, the estimator takes the median of $2t-1$ independent estimates, and the probability that the estimate is off by more than $2 \|v\|_2/\sqrt{s}$ is exponentially small in $t$.
no code implementations • 16 Jul 2022 • Christoph Grunau, Ahmet Alper Özüdoğru, Václav Rozhoň, Jakub Tětek
In their seminal work, Arthur and Vassilvitskii [SODA 2007] asked about the guarantees for its following \emph{greedy} variant: in every step, we sample $\ell$ candidate centers instead of one and then pick the one that minimizes the new cost.
no code implementations • 6 Jan 2023 • Christian Janos Lebeda, Jakub Tětek
Chan, Li, Shi, and Xu [PETS 2012] describe a differentially private version of the Misra-Gries sketch, but the amount of noise it adds can be large and scales linearly with the size of the sketch: the more accurate the sketch is, the more noise this approach has to add.
