Add to ORKGThe frequency moments of a sequence containing mi elements of type i, for 1 i n, are the numbers Fk = Pn m i=1 k i. We consider the space complexity of randomized algorithms that approximate the numbers Fk, when the elements of the sequence are given one by one and cannot be stored. Surprisingly, it turns out that the numbers F0�F1 and F2 can be approximated in logarithmic space, whereas the approximation of Fk for k 6 requires n (1) space. Applications to data bases are mentioned as well
Abstract Kernel methods represent one of the most powerful tools in machine learning to tackle probl...
We derive new time-space tradeoff lower bounds and algorithms for exactly computing statistics of in...
We prove that, with high probability, the space complexity of refuting a random unsatisfiable Boolea...
AbstractThe frequency moments of a sequence containingmielements of typei, 1⩽i⩽n, are the numbersFk=...
In 1999 Alon et al. introduced the still active research topic of approximating the frequency moment...
Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for...
We give a one-pass, O~(m^{1-2/k})-space algorithm for estimating the k-th frequency moment of a data...
Recently, an extension of the standard data stream model has been introduced in which an algorithm c...
We give a 1-pass Õ(m1−2/k)-space algorithm for computing the k-th frequency moment of a data stream ...
In this paper, we provide the first optimal algorithm for the remaining open question from the semin...
We consider the read/write streams model, an extension of the standard data stream model in which an...
Given the widespread use of lossless compression algorithms to approximate algorithmic (Kolmogorov-C...
We study the complexity of approximating the VC dimension of a collection of sets, when the sets are...
This is a presentation about joint work between Hector Zenil and Jean-Paul Delahaye. Zenil presents ...
AbstractWe show that any randomized algorithm that runs in spaceSand timeTand uses poly(S) random bi...
Abstract Kernel methods represent one of the most powerful tools in machine learning to tackle probl...
We derive new time-space tradeoff lower bounds and algorithms for exactly computing statistics of in...
We prove that, with high probability, the space complexity of refuting a random unsatisfiable Boolea...
AbstractThe frequency moments of a sequence containingmielements of typei, 1⩽i⩽n, are the numbersFk=...
In 1999 Alon et al. introduced the still active research topic of approximating the frequency moment...
Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for...
We give a one-pass, O~(m^{1-2/k})-space algorithm for estimating the k-th frequency moment of a data...
Recently, an extension of the standard data stream model has been introduced in which an algorithm c...
We give a 1-pass Õ(m1−2/k)-space algorithm for computing the k-th frequency moment of a data stream ...
In this paper, we provide the first optimal algorithm for the remaining open question from the semin...
We consider the read/write streams model, an extension of the standard data stream model in which an...
Given the widespread use of lossless compression algorithms to approximate algorithmic (Kolmogorov-C...
We study the complexity of approximating the VC dimension of a collection of sets, when the sets are...
This is a presentation about joint work between Hector Zenil and Jean-Paul Delahaye. Zenil presents ...
AbstractWe show that any randomized algorithm that runs in spaceSand timeTand uses poly(S) random bi...
Abstract Kernel methods represent one of the most powerful tools in machine learning to tackle probl...
We derive new time-space tradeoff lower bounds and algorithms for exactly computing statistics of in...
We prove that, with high probability, the space complexity of refuting a random unsatisfiable Boolea...