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Randomized Algorithms for the Loop Cutset Problem
We show how to find a minimum weight loop cutset in a Bayesian network w...
06/01/2011 ∙ by R. Bar-Yehuda, et al. ∙
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Sampling Humans for Optimizing Preferences in Coloring Artwork
Many circumstances of practical importance have performance or success m...
06/10/2019 ∙ by Michael McCourt, et al. ∙ 0
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Semantic Human Matting
Human matting, high quality extraction of humans from natural images, is...
09/05/2018 ∙ by Quan Chen, et al. ∙ 0
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Feedback Scheduling for Energy-Efficient Real-Time Homogeneous Multiprocessor Systems
Real-time scheduling algorithms proposed in the literature are often bas...
06/08/2016 ∙ by Mason Thammawichai, et al. ∙ 0
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Human-Centric Data Cleaning [Vision]
Data Cleaning refers to the process of detecting and fixing errors in th...
12/24/2017 ∙ by El Kindi Rezig, et al. ∙ 0
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Can the Optimizer Cost be Used to Predict Query Execution Times?
Predicting the execution time of queries is an important problem with ap...
05/02/2019 ∙ by Anthony Kleerekoper, et al. ∙ 0
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Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation
Humans have an unparalleled visual intelligence and can overcome visual ...
02/05/2018 ∙ by Mohamed El Banani, et al. ∙ 0
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Formalizing Interruptible Algorithms for Human over-the-loop Analytics
12/03/2017 ∙ by Austin Graham, et al. ∙
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Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans "in-the-loop" have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans "over-the-loop", giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.
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