AMAranyak Mehta
Aranyak Mehta
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Aranyak Mehta has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
Evangelos Markakis
Department of Mathe- matics Univ. of Crete GR-71409 Iraklio Greece
1 shared publication- Mihail N. Kolountzakis
Department of Mathematics and Applied Mathematics, University of Crete, Heraklion, Greece
1 shared publication - Scott Shenker
UCLA UCLA UC Berkeley and ICSI UCLA
1 shared publication - Vijay V. Vazirani
University of California, Irvine, Irvine, USA
1 shared publication
Latest Publications
- 28 February 2006
- Published by Elsevier in Journal of Algorithms
- Vol. 58 (2) , 150-164
Posted price profit maximization for multicast by approximating fixed points
- 28 February 2006
- Published by Elsevier in Journal of Algorithms
- Vol. 58 (2) , 150-164
Posted price profit maximization for multicast by approximating fixed points
- Open Access
Learning symmetric k-juntas in time n^o(k)
We give an algorithm for learning symmetric k-juntas (boolean functions of $n$ boolean variables which depend only on an unknown set of $k$ of these variables) in the PAC model under the uniform distribution, which runs in time n^{O(k/\log k)}. Our bound is obtained by proving the following result: Every symmetric boolean function on k variables, except for the parity and the constant functions, has a non-zero Fourier coefficient of order at least 1 and at most O(k/\log k). This improves the previously best known bound of (3/31)k, and provides the first n^{o(k)} time algorithm for learning symmetric juntas.Open AccessLearning symmetric k-juntas in time n^o(k)
We give an algorithm for learning symmetric k-juntas (boolean functions of $n$ boolean variables which depend only on an unknown set of $k$ of these variables) in the PAC model under the uniform distribution, which runs in time n^{O(k/\log k)}. Our bound is obtained by proving the following result: Every symmetric boolean function on k variables, except for the parity and the constant functions, has a non-zero Fourier coefficient of order at least 1 and at most O(k/\log k). This improves the previously best known bound of (3/31)k, and provides the first n^{o(k)} time algorithm for learning symmetric juntas.