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pro vyhledávání: '"Chen, Xinjia"'
Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models. The existing clustering methods generally aggregate the features within subgraphs that are often implemente
Externí odkaz:
http://arxiv.org/abs/2304.10831
Autor:
Chen, Xinjia
Publikováno v:
Proc. SPIE 11425, Unmanned Systems Technology XXII, 114250U (23 April 2020)
In many areas of engineering and sciences, decision rules and control strategies are usually designed based on nominal values of relevant system parameters. To ensure that a control strategy or decision rule will work properly when the relevant param
Externí odkaz:
http://arxiv.org/abs/2005.06277
Autor:
Chen, Xinjia
In this paper, we develop a general theory of truncated inverse binomial sampling. In this theory, the fixed-size sampling and inverse binomial sampling are accommodated as special cases. In particular, the classical Chernoff-Hoeffding bound is an im
Externí odkaz:
http://arxiv.org/abs/1908.06907
Akademický článek
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Autor:
Chen, Xinjia
We propose a geometric approach for bounding average stopping times for stopped random walks in discrete and continuous time. We consider stopping times in the hyperspace of time indexes and stochastic processes. Our techniques relies on exploring ge
Externí odkaz:
http://arxiv.org/abs/1507.03245
Autor:
Chen, Xinjia
We explore the applications of our previously established likelihood-ratio method for deriving concentration inequalities for a wide variety of univariate and multivariate distributions. New concentration inequalities for various distributions are de
Externí odkaz:
http://arxiv.org/abs/1409.6276
Autor:
Chen, Xinjia
We propose new generalized multivariate hypergeometric distributions, which extremely resemble the classical multivariate hypergeometric distributions. The proposed distributions are derived based on an urn model approach. In contrast to existing met
Externí odkaz:
http://arxiv.org/abs/1309.0805
Autor:
Chen, Xinjia
We derive simple concentration inequalities for bounded random vectors, which generalize Hoeffding's inequalities for bounded scalar random variables. As applications, we apply the general results to multinomial and Dirichlet distributions to obtain
Externí odkaz:
http://arxiv.org/abs/1309.0003
Autor:
Chen, Xinjia
We propose a new approach for deriving probabilistic inequalities based on bounding likelihood ratios. We demonstrate that this approach is more general and powerful than the classical method frequently used for deriving concentration inequalities su
Externí odkaz:
http://arxiv.org/abs/1308.4123
Autor:
Chen, Xinjia
A large class of problems in sciences and engineering can be formulated as the general problem of constructing random intervals with pre-specified coverage probabilities for the mean. Wee propose a general approach for statistical inference of mean v
Externí odkaz:
http://arxiv.org/abs/1306.2290