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Efficient Learning for Clustering and Optimizing Context-Dependent Designs
We consider a simulation optimization problem for a context-dependent de...
12/10/2020 ∙ by Haidong Li, et al. ∙
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Knowledge Gradient for Selection with Covariates: Consistency and Computation
Knowledge gradient is a design principle for developing Bayesian sequent...
06/12/2019 ∙ by Xiaowei Zhang, et al. ∙
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Ranking and Selection as Stochastic Control
Under a Bayesian framework, we formulate the fully sequential sampling a...
10/07/2017 ∙ by Yijie Peng, et al. ∙
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Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
This paper is concerned with identifying contexts useful for training wo...
08/19/2016 ∙ by Ivan Vulić, et al. ∙
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Context Exploitation using Hierarchical Bayesian Models
We consider the problem of how to improve automatic target recognition b...
05/30/2018 ∙ by Christopher A. George, et al. ∙
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Ranking and Selection with Covariates for Personalized Decision Making
We consider a ranking and selection problem in the context of personaliz...
10/07/2017 ∙ by Haihui Shen, et al. ∙
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Deep architectures for learning context-dependent ranking functions
Object ranking is an important problem in the realm of preference learni...
03/15/2018 ∙ by Karlson Pfannschmidt, et al. ∙
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Context-dependent Ranking and Selection under a Bayesian Framework
12/10/2020 ∙ by Haidong Li, et al. ∙
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We consider a context-dependent ranking and selection problem. The best design is not universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic sampling scheme for context-dependent optimization (DSCO) to efficiently learn and select the best designs in all contexts. The proposed sampling scheme is proved to be consistent. Numerical experiments show that the proposed sampling scheme significantly improves the efficiency in context-dependent ranking and selection.
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