Machine learning Archives – H. Paul Keeler

My collaborators and I uploaded a manuscript:

Błaszczyszyn, Brochard, and Keeler, Coverage probability in wireless networks with determinantal scheduling.

https://arxiv.org/abs/2006.05038

Details

The paper builds off some previous work by us that uses a (relatively) new model in machine learning:

Błaszczyszyn and Keeler, Determinantal thinning of point processes with network learning applications.

https://arxiv.org/abs/1810.08672

The new machine learning model is based on a special type of point process called a determinantal point process. It was originally called a Fermion point process. These are useful point processes as the exhibit certain closure properties under certain operations such independent thinning.

Kulesza and Taskar introduced and developed the framework for using determinantal point processes for machine learning models.

Code

The MATLAB code for the producing the results in the paper can be found here:

https://github.com/hpaulkeeler/detcov_matlab

I also re-wrote the MATLAB code into Python:

https://github.com/hpaulkeeler/detcov_python

Details

The paper uses a (relatively) new model framework in machine learning. This framework is based on a special type of point process called a determinantal point process, which is also called a fermion point process. (This particle model draw inspiration from the form of the wave function in quantum mechanics.) Kulesza and Taskar introduced and developed the framework for using determinantal point processes for machine learning models.