Active diagnosis under persistent noise with unknown noise distribution: A rank-based approach

Gowtham Bellala, Suresh Bhavnani, Clayton Scott

Research output: Contribution to journalArticle

Abstract

We consider a problem of active diagnosis, where the goal is to efficiently identify an unknown object by sequentially selecting, and observing, the responses to binary valued queries. We assume that query observations are noisy, and further that the noise is persistent, meaning that repeating a query does not change the response. Previous work in this area either assumed the knowledge of the query noise distribution, or that the noise level is sufficiently low so that the unknown object can be identified with high accuracy. We make no such assumptions, and introduce an algorithm that returns a ranked list of objects, such that the expected rank of the true object is optimized. Furthermore, our algorithm does not require knowledge of the query noise distribution.

Original languageEnglish (US)
Pages (from-to)155-163
Number of pages9
JournalJournal of Machine Learning Research
Volume15
StatePublished - 2011

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Query
Unknown
High Accuracy
Binary
Object
Knowledge

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Cite this

Active diagnosis under persistent noise with unknown noise distribution : A rank-based approach. / Bellala, Gowtham; Bhavnani, Suresh; Scott, Clayton.

In: Journal of Machine Learning Research, Vol. 15, 2011, p. 155-163.

Research output: Contribution to journalArticle

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