no code implementations • 14 Jul 2022 • Liat Cohen, Tal Grinshpoun, Gera Weiss
We present an efficient algorithm that, given a discrete random variable $X$ and a number $m$, computes a random variable whose support is of size at most $m$ and whose Kolmogorov distance from $X$ is minimal, also for the one-sided Kolmogorov approximation.
no code implementations • 25 Oct 2019 • Dengji Zhao, Yiqing Huang, Liat Cohen, Tal Grinshpoun
The research on coalitional games has focused on how to share the reward among a coalition such that players are incentivised to collaborate together.
no code implementations • 19 May 2018 • Liat Cohen, Dror Fried, Gera Weiss
We present an algorithm that takes a discrete random variable $X$ and a number $m$ and computes a random variable whose support (set of possible outcomes) is of size at most $m$ and whose Kolmogorov distance from $X$ is minimal.
no code implementations • 4 Mar 2015 • Liat Cohen, Solomon Eyal Shimony, Gera Weiss
For the proposed approximation algorithm, we establish formal approximation bounds and show that the time and memory complexities grow polynomially with the required accuracy, the number of nodes in the plan, and with the size of the support of the random variables that represent the durations of the primitive tasks.
