StanfordASL / ALPaCA

Code for "Meta-Learning Priors for Efficient Online Bayesian Regression" by James Harrison, Apoorva Sharma, and Marco Pavone

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ALPaCA

Code for "Meta-Learning Priors for Efficient Online Bayesian Regression" by James Harrison, Apoorva Sharma, and Marco Pavone

Installation

To install requirements, run

pip install -r requirements.txt

MuJoCo is required for Hopper experiment.

Running the code

The experiments presented in the paper can be run from the jupyter notebooks.

About

Code for "Meta-Learning Priors for Efficient Online Bayesian Regression" by James Harrison, Apoorva Sharma, and Marco Pavone

License:MIT License


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Language:Jupyter Notebook 70.9%Language:Python 28.9%Language:Dockerfile 0.1%Language:Shell 0.0%Language:Makefile 0.0%