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pro vyhledávání: '"Murua, Alejandro"'
Uplift is a particular case of conditional treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention or a medical treatment. In practice, these models are built on individual d
Externí odkaz:
http://arxiv.org/abs/2105.05146
Autor:
Vicente, Serge, Murua, Alejandro
Determinantal consensus clustering is a promising and attractive alternative to partitioning about medoids and k-means for ensemble clustering. Based on a determinantal point process or DPP sampling, it ensures that subsets of similar points are less
Externí odkaz:
http://arxiv.org/abs/2102.03954
Autor:
Vicente, Serge, Murua, Alejandro
Random restart of a given algorithm produces many partitions to yield a consensus clustering. Ensemble methods such as consensus clustering have been recognized as more robust approaches for data clustering than single clustering algorithms. We propo
Externí odkaz:
http://arxiv.org/abs/2102.03948
Recurrent neural networks (RNN) such as long-short-term memory (LSTM) networks are essential in a multitude of daily live tasks such as speech, language, video, and multimodal learning. The shift from cloud to edge computation intensifies the need to
Externí odkaz:
http://arxiv.org/abs/2006.05442
Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity only if targe
Externí odkaz:
http://arxiv.org/abs/1911.12474
Uplift modeling aims at predicting the causal effect of an action such as a medical treatment or a marketing campaign on a particular individual, by taking into consideration the response to a treatment. The treatment group contains individuals who a
Externí odkaz:
http://arxiv.org/abs/1901.10867
Publikováno v:
Annals of Applied Statistics 2015, Vol. 9, No. 3, 1643-1670
We propose and develop a Bayesian plaid model for biclustering that accounts for the prior dependency between genes (and/or conditions) through a stochastic relational graph. This work is motivated by the need for improved understanding of the molecu
Externí odkaz:
http://arxiv.org/abs/1511.05375
Publikováno v:
Journal of Computational and Graphical Statistics, 2017 Jun 01. 26(2), 265-274.
Externí odkaz:
https://www.jstor.org/stable/44861952
Akademický článek
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Autor:
Murua, Alejandro, Wicker, Nicolas
Publikováno v:
Journal of Computational and Graphical Statistics, 2014 Sep 01. 23(3), 717-739.
Externí odkaz:
https://www.jstor.org/stable/43304919