# Optimal Online Data Sampling or How to Hire the Best Secretaries

@article{Girdhar2009OptimalOD, title={Optimal Online Data Sampling or How to Hire the Best Secretaries}, author={Yogesh A. Girdhar and Gregory Dudek}, journal={2009 Canadian Conference on Computer and Robot Vision}, year={2009}, pages={292-298} }

The problem of online sampling of data, can be seen as a generalization of the classical secretary problem. The goal is to maximize the probability of picking the k highest scoring samples in our data, making the decision to select or reject a sample on-line. We present a new and simple on-line algorithm to optimally make this selection. We then apply this algorithm to a sequence of images taken by a mobile robot, with the goal of identifying the most interesting and informative images.

## Figures and Topics from this paper

## 16 Citations

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A novel on-line algorithm is presented based on a new extension to the classical Secretaries Problem based on the idea of Bayesian Surprise, which is then used to measure the fitness of an image as a summary image.

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We consider a variant of the classical Secretary Problem. In this setting, the candidates are ranked according to some exchangeable random variable and the quest is to maximize the expected quality…

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- 2015

This work considers a case of this sequential choice problem in which the interviewer has a finite, known number of choices and can only discern the relative ranks of choices, and in which every permutation of ranks is equally likely.

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The aim of the paper is to give a generalised model in such a way that the chosen set of the possible best k elements have to be independent of all previously rejected elements.

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- Computer Science2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)
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An algorithm is suggested, Sampling and Adaptive Monitoring (SAM), to perform both the tasks of approximating the environmental field and sampling from the hotspots, simultaneously, simultaneously.

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