Generalized Linear Models

Linear Regression

Linear Regression is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is least squares, where we minimize the mean square error between the predicted values $\hat{y} = \textbf{X}\hat{\beta}$ and actual values $y$: $\left(y-\textbf{X}\beta\right)^{2}$.

We can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\hat{\beta}$.

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Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
regression 232 36.36%
In-Context Learning 33 5.17%
Prediction 20 3.13%
Continual Learning 11 1.72%
Causal Inference 11 1.72%
Uncertainty Quantification 9 1.41%
Decision Making 9 1.41%
Federated Learning 8 1.25%
Computational Efficiency 8 1.25%

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