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We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we formulate the optimal dynamic sampling decision as a stochastic dynamic programming problem, and develop a sequential sampling policy to efficiently learn the performance of each design under each context. The asymptotically optimal sampling ratios are derived to attain the optimal large deviations rate of the worst-case of probability of false selection. The proposed sampling policy is proved to be consistent and its asymptotic sampling ratios are asymptotically optimal. Numerical experiments demonstrate that the proposed method improves the efficiency for selection of top-m context-dependent designs.

Learning in the presence of label noise is a promising research issue, which can well accommodate to the reality of weakly supervised annotations in the era of big data. The existing methodologies to address such issue mainly focus on either roughly preprocessing on the collected dataset or performing modifications on original model, but the latter relies on strict assumptions about noise generation process, and parameters are tough to tune or estimate. In this paper, we report an energy inspired model in label denoising application, which can well characterize relationships between collected data and latent labels and act as a noisy label filter. Meanwhile, the local proximities in patterns are believed to be beneficial in repairing wrong labels, and the experiments on some typical synthetic dataset demonstrate its great power in label denoising. Our proposed model has better explanations and can be flexibly extended to multi-class cases.

An organization is a collection of functional members for a specific purpose and their relationships. In reality, various tangible and intangible organizations affect all aspects of people's lives. This paper proposes F-statistics for group division to evaluate and define the difference degrees of groups. The results can be used as an indicator of the group number estimation, and to verify the effectiveness of the F-statistics based group number estimation method.