Learning $k$-Modal Distributions via Testing

  title={Learning \$k\$-Modal Distributions via Testing},
  author={Constantinos Daskalakis and Ilias Diakonikolas and Rocco A. Servedio},
  journal={Theory of Computing},
A k-modal probability distribution over the domain {1, ..., n} is one whose histogram has at most k “peaks” and “valleys.” Such distributions are natural generalizations of monotone (k = 0) and unimodal (k = 1) probability distributions, which have been intensively studied in probability theory and statistics. In this paper we consider the problem of learning an unknown k-modal distribution. The learning algorithm is given access to independent samples drawn from the k-modal distribution p, and… CONTINUE READING
Highly Cited
This paper has 79 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 1 time. VIEW TWEETS

From This Paper

Topics from this paper.


Publications citing this paper.

79 Citations

Citations per Year
Semantic Scholar estimates that this publication has 79 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 26 references

Highlights of the Bertinoro workshop on Sublinear Algorithms (unpublished comments)

O. Goldreich
Posted at http://www.wisdom.weizmann.ac.il/õded/MC/072.html, accessed June • 2011

Property Testing: A Learning Theory Perspective

Foundations and Trends in Machine Learning • 2007
View 1 Excerpt

Combinatorial methods in density estimation

L. Devroye, G. Lugosi

Estimation of unimodal densities without smoothness assumptions

L. Birgé
Annals of Statistics, • 1997

Nonasymptotic universal smoothing factors, kernel complexity and Yatracos classes

L. Devroye, G. Lugosi
Annals of Statistics, • 1996

Similar Papers

Loading similar papers…