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Implementation of Fair Regression with Wasserstein Barycenters.ipynb

Implementation of Fair Regression with Wasserstein Barycenters.ipynb

 
 

Implementation of Fair Regression with Wasserstein Barycenters.py

Implementation of Fair Regression with Wasserstein Barycenters.py

 
 

LICENSE

LICENSE

 
 

README.md

README.md

 
 

multi-dimensional test CRIME.ipynb

multi-dimensional test CRIME.ipynb

 
 

test Adult.ipynb

test Adult.ipynb

 
 

test Adult.py

test Adult.py

 
 

test COMPAS.ipynb

test COMPAS.ipynb

 
 

test COMPAS.py

test COMPAS.py

 
 

test CRIME 2.ipynb

test CRIME 2.ipynb

 
 

test CRIME.ipynb

test CRIME.ipynb

 
 

test CRIME.py

test CRIME.py

 
 

test LSAC 2.ipynb

test LSAC 2.ipynb

 
 

test LSAC.ipynb

test LSAC.ipynb

 
 

test LSAC.py

test LSAC.py

 
 

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fair_data_representation

In this repository, we provide both the implementation of proposed algorithms and the experimental results in

"Fair Data Representation for Machine Learning at Pareto Frontier" by Shizhou Xu and Thomas Strohmer in 2021.

The experiment results are based on the following data sets:

  1. Adult: UCI Adult Data Set
  2. COMPAS: Correctional Offender Management Profiling for Alternative Sanctions
  3. CRIME: Communities and Crime Data Set
  4. LSAC: LSAC National Longitudinal Bar Passage Study data set

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We study synthetic data via which the trained machine learning models results in fair outcome.

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