Unsupervised cost sensitive predictions with side information
Pages 364 - 367
Abstract
In many security and healthcare systems, a sequence of sensors/tests is used for detection and diagnosis. Each test outputs a prediction of the latent state, and carries with it inherent costs. Our objective is to learn strategies for selecting a test that gives the best trade-off between accuracy & costs. Unfortunately, it is often impossible to acquire ground truth annotations and we are left with the problem of unsupervised sensor selection (USS). Hanawal et al. [9] reduces USS to a special case of multi-armed bandit problem with side information and develop polynomial time algorithms that achieve sub-linear regret. In this paper, we extend earlier analysis with contextual information, propose an algorithm having sub-linear regret and verify our results on synthetic & real datasets.
References
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Index Terms
- Unsupervised cost sensitive predictions with side information
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Information & Contributors
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Published In
January 2018
379 pages
ISBN:9781450363419
DOI:10.1145/3152494
- Conference Chair:
- Sayan Ranu,
- General Chairs:
- Niloy Ganguly,
- Raghu Ramakrishnan,
- Program Chairs:
- Sunita Sarawagi,
- Shourya Roy
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Association for Computing Machinery
New York, NY, United States
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Published: 11 January 2018
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CoDS-COMAD '18
CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data
January 11 - 13, 2018
Goa, India
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CODS-COMAD '18 Paper Acceptance Rate 50 of 150 submissions, 33%;
Overall Acceptance Rate 197 of 680 submissions, 29%
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