Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach

Gowtham Bellala, Suresh Bhavnani, Clayton Scott
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:155-163, 2011.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-bellala11a, title = {Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach}, author = {Bellala, Gowtham and Bhavnani, Suresh and Scott, Clayton}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {155--163}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/bellala11a/bellala11a.pdf}, url = {https://proceedings.mlr.press/v15/bellala11a.html}, 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.} }
Endnote
%0 Conference Paper %T Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach %A Gowtham Bellala %A Suresh Bhavnani %A Clayton Scott %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-bellala11a %I PMLR %P 155--163 %U https://proceedings.mlr.press/v15/bellala11a.html %V 15 %X 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.
RIS
TY - CPAPER TI - Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach AU - Gowtham Bellala AU - Suresh Bhavnani AU - Clayton Scott BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-bellala11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 155 EP - 163 L1 - http://proceedings.mlr.press/v15/bellala11a/bellala11a.pdf UR - https://proceedings.mlr.press/v15/bellala11a.html AB - 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. ER -
APA
Bellala, G., Bhavnani, S. & Scott, C.. (2011). Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:155-163 Available from https://proceedings.mlr.press/v15/bellala11a.html.

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