Zobrazeno 1 - 10
of 5 413
pro vyhledávání: '"Vana, A."'
Package spar for R builds ensembles of predictive generalized linear models with high-dimensional predictors. It employs an algorithm utilizing variable screening and random projection tools to efficiently handle the computational challenges associat
Externí odkaz:
http://arxiv.org/abs/2411.17808
Autor:
Vana-Gür, Laura, Hirk, Rainer
In this paper we build a joint model which can accommodate for binary, ordinal and continuous responses, by assuming that the errors of the continuous variables and the errors underlying the ordinal and binary outcomes follow a multivariate normal di
Externí odkaz:
http://arxiv.org/abs/2411.02924
In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, a
Externí odkaz:
http://arxiv.org/abs/2410.19215
In recent years, serverless computing, especially Function as a Service (FaaS), is rapidly growing in popularity as a cloud programming model. The serverless computing model provides an intuitive interface for developing cloud-based applications, whe
Externí odkaz:
http://arxiv.org/abs/2410.06721
Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive, event-based i
Externí odkaz:
http://arxiv.org/abs/2410.06695
The radical advances in mobile computing, the IoT technological evolution along with cyberphysical components (e.g., sensors, actuators, control centers) have led to the development of smart city applications that generate raw or pre-processed data,
Externí odkaz:
http://arxiv.org/abs/2410.18106
We address the challenge of correlated predictors in high-dimensional GLMs, where regression coefficients range from sparse to dense, by proposing a data-driven random projection method. This is particularly relevant for applications where the number
Externí odkaz:
http://arxiv.org/abs/2410.00971
A first proposal of a sparse and cellwise robust PCA method is presented. Robustness to single outlying cells in the data matrix is achieved by substituting the squared loss function for the approximation error by a robust version. The integration of
Externí odkaz:
http://arxiv.org/abs/2408.15612
In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bay
Externí odkaz:
http://arxiv.org/abs/2404.16560
The imminent need to interpret the output of a Machine Learning model with counterfactual (CF) explanations - via small perturbations to the input - has been notable in the research community. Although the variety of CF examples is important, the asp
Externí odkaz:
http://arxiv.org/abs/2404.13476