Learning k-modal Distributions via Testing

Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 must output a hypothesis distribution p such that with high probability the total variation distance between p and p is at most ε.

We give an efficient algorithm for this problem that runs in time poly(k, log(n), 1/ε). For k ≤ Õ(√ log n), the number of samples used by our algorithm is very close (within an Õ(log(1/ε)) factor) to being information-theoretically optimal. Prior to this work computationally efficient algorithms were known only for the cases k = 0, 1 [Bir87b, Bir97].

A novel feature of our approach is that our learning algorithm crucially uses a new property testing algorithm as a key subroutine. The learning algorithm uses the property tester to efficiently decompose the k-modal distribution into k (near)-monotone distributions, which are easier to learn.
Original languageEnglish
Title of host publicationProceedings of the Twenty-third Annual ACM-SIAM Symposium on Discrete Algorithms
Number of pages15
Publication statusPublished - 2012

Publication series

NameSODA '12


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