Experimental evaluation of the results presented in
"Fast rates for noisy interpolation require rethinking the effects of inductive bias"
Donhauser K., Ruggeri N., Stojanovic S., Yang F.
In all the experiments we utilize Python 3.8.3.
Any additional package requirements are listed in requirements.txt, and can be installed via command line
pip install requirements.txt
For convex optimization problems, we rely on the CVXPY package. A variety of
open-source solvers are available
here.
Any solver can be specified via the --solver command line argument in main_lp_svc.py and main_lp_svr.py.
In our experiments we rely on Mosek, which we observed to be faster and more accurate than other convex programming solvers. While Mosek is a commercial product, they provide free academic licenses for research or educational purposes at degree-granting academic institutions to faculty, students, and staff. To obtain a license, visit https://www.mosek.com/products/academic-licenses/ and register using your email address from your academic institution.
The solver will by default assume the license file to be in $HOME/mosek/mosek.lic. The path can be changed by setting
the MOSEKLM_LICENSE_FILE environment variable. Refer to the
Mosek website
for further installation instructions.
For replicating the classification experiments in the paper, run
python main_lp_svr.py --dataset X --p X --label_noise X
where the parameters p and label_noise can be specified.
The dataset can be chosen between leukemia and synthetic_n=100_d=5000.
For synthetic data, the values of n anfd d can be customized.
Similarly, the regression experiments can be replicated by specifying p and noise in
python main_lp_svr.py --dataset synthetic_n=100_d=5000 --p X --noise X
To replicate the CNTK experiments, specify the parameters in
python main_cntk.py --kernel_size X --depth X --label_noise X