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[JMLR] Reproducible code for Optimal Locally Private Nonparametric Classification with Public Data

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6 Commits
LDPCP
LDPCP
 
 
comparison
comparison
 
 
data
data
 
 
distribution
distribution
 
 
results
results
 
 
tests
tests
 
 
.DS_Store
.DS_Store
 
 
README.md
README.md
 
 
execute.sh
execute.sh
 
 
plot.ipynb
plot.ipynb
 
 
realdata.py
realdata.py
 
 
simulation_depth.py
simulation_depth.py
 
 
simulation_epsilon_acc.py
simulation_epsilon_acc.py
 
 
simulation_gamma.py
simulation_gamma.py
 
 
simulation_lamda.py
simulation_lamda.py
 
 
simulation_local_gamma.py
simulation_local_gamma.py
 
 
simulation_np_acc.py
simulation_np_acc.py
 
 
simulation_npnq.py
simulation_npnq.py
 
 
simulation_nq_acc.py
simulation_nq_acc.py
 
 
simulation_partition.py
simulation_partition.py
 
 
simulation_prune.py
simulation_prune.py
 
 
simulation_range_parameter.py
simulation_range_parameter.py
 
 
summarize.ipynb
summarize.ipynb
 
 

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Reproducible code for Optimal Locally Private Nonparametric Classification with Public Data. To run the code, install the last version of numpy, pandas, scikit-learn, itertools, numba. Then execute execute.sh.

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[JMLR] Reproducible code for Optimal Locally Private Nonparametric Classification with Public Data

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