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Experimental evaluation of the results in our paper "Fast rates for noisy interpolation require rethinking the effects of inductive bias"

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Fast rates for noisy interpolation require rethinking the effects of inductive bias

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.

drawing

Requirements

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

Convex solvers

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.

Experiments

Classification: synthetic and Leukemia datasets

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.

Regression: synthetic data

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

Convolutional Neural Tangent Kernel on Binarized MNIST

To replicate the CNTK experiments, specify the parameters in

python main_cntk.py --kernel_size X --depth X --label_noise X

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Experimental evaluation of the results in our paper "Fast rates for noisy interpolation require rethinking the effects of inductive bias"

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