Welcome to the RFF-BLR repository! This repository houses the implementation of the Random Fourier Features Bayesian Linear Regression (RFF-BLR) presented in the paper Bayesian learning of feature spaces for multitask problems.
In this repository, we delve into the world of advanced regression techniques with a focus on Bayesian inference and the Random Fourier Features. We provide an in-depth exploration through an ablation study, comparing various model extensions such as Bayesian formulation, Links, and Gamma optimization. Additionally, we conduct a rigorous comparison against different baselines using synthetic datasets, shedding light on the profound impact of RFF and its interaction with Bayesian Sparse formulations. Lastly, we offer a computational cost analysis, illuminating the efficiency of the RFF-BLR model in diverse scenarios using synthetic datasets.
To get started, follow these simple installation steps:
# Clone the repository
git clone https://github.com/sevisal/RFF-BLR.git
# Navigate into the cloned directory
cd RFF-BLR
# Install the required Python packages
pip install -r requirements.txt
This repository offers various scripts designed for different aspects of the study:
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Baseline Comparison: Run the
BaselineComparison.py
script to initiate the baseline comparison:python BaselineComparison.py
Witness the comparative performance of the RFF-BLR model against other baseline models using synthetic datasets.
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Ablation Study: Execute the
AblationStudy.py
script to run the ablation study:python AblationStudy.py
Dive into the realm of model extensions with functions meticulously crafted for each combination, exploring Bayesian formulations with gamma optimization or with RVFL-like links.
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Computational Cost Analysis: To run the computational cost analysis, execute the
CostAnalysis.py
script:python CostAnalysis.py
Experience a detailed analysis of computational costs, providing invaluable insights into the performance of the RFF-BLR model using synthetic datasets.