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The learning curves of optimisation algorithms, plotting the evolution of the objective vs. runtime spent, can be viewed as a sample of longitudinal data. In this paper we describe mixed-effects modeling, a standard technique in longitudinal data analysis, and give an example of its application to algorithm performance modeling.Anglai
Generalized mixed-effects model of the effects of declarative learning ability, procedural learning ...
Modern high-performing algorithms are usually highly parameterised, and can be configured either man...
Master's thesis in Mathematics and PhysicsThe Linear mixed effects model is based on one of the assu...
The learning curves of optimisation algorithms, plotting the evolution of the objective vs. runtime ...
We consider predictions in longitudinal studies, and investigate the well known statistical mixed-ef...
Linear Mixed Model (LMM) is an extended regression method that is used for longitudinal data which h...
Linear mixed effects methods for the analysis of longitudinal data provide a convenient framework fo...
The concern of this thesis is to discuss classification of different objects based on longitudinal o...
BACKGROUND: Linear mixed effects models (LMMs) are a common approach for analyzing longitudinal data...
Three factors enter into analyses of performance curves such as learning curves: the amount of train...
Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth stat...
Mixed-effect modeling is recommended for data with repeated measures, as often encountered in design...
AbstractExperimental evaluations of speedup learning methods have in the past used non-parametric hy...
Mixed models have become important in analyzing the results of experiments, particularly those that ...
Mixed models have become important in analyzing the results of experiments, particularly those that ...
Generalized mixed-effects model of the effects of declarative learning ability, procedural learning ...
Modern high-performing algorithms are usually highly parameterised, and can be configured either man...
Master's thesis in Mathematics and PhysicsThe Linear mixed effects model is based on one of the assu...
The learning curves of optimisation algorithms, plotting the evolution of the objective vs. runtime ...
We consider predictions in longitudinal studies, and investigate the well known statistical mixed-ef...
Linear Mixed Model (LMM) is an extended regression method that is used for longitudinal data which h...
Linear mixed effects methods for the analysis of longitudinal data provide a convenient framework fo...
The concern of this thesis is to discuss classification of different objects based on longitudinal o...
BACKGROUND: Linear mixed effects models (LMMs) are a common approach for analyzing longitudinal data...
Three factors enter into analyses of performance curves such as learning curves: the amount of train...
Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth stat...
Mixed-effect modeling is recommended for data with repeated measures, as often encountered in design...
AbstractExperimental evaluations of speedup learning methods have in the past used non-parametric hy...
Mixed models have become important in analyzing the results of experiments, particularly those that ...
Mixed models have become important in analyzing the results of experiments, particularly those that ...
Generalized mixed-effects model of the effects of declarative learning ability, procedural learning ...
Modern high-performing algorithms are usually highly parameterised, and can be configured either man...
Master's thesis in Mathematics and PhysicsThe Linear mixed effects model is based on one of the assu...
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