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python implementation of online learning to learn non-smooth algorithms
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README.md
algorithms.py
exp_lenk.py
exp_mulan.py
exp_school.py
exp_synthetic.py
experiments.py
grid_search.py
lenk_debug.py
losses.py
plots.py
plotsv2.ipynb
prox.py
train.py
utils.py

README.md

onlineLTL

python implementation of online learning to learn non-smooth algorithms.

Requirements

This repository requires python 3.x, numpy, pandas, scipy and sci-kit learn.

Exps from "Learning-to-Learn Stochastic Gradient Descent with Biased Regularization"

This repo contains the code for the experiments of the paper "Learning-to-Learn Stochastic Gradient Descent with Biased Regularization" (https://arxiv.org/abs/1903.10399v1)

For the synthetic experiments run exp_synthetic.py while for the computer survey experiments run exp_lenk.py.

You can find the implementation of the algorithms discussed in the paper inside algorithms.py, while the dataset generation and loading functions are in data/data_generator.py and data/data_load.py

Experiments results will be stored in a folder inside exps with a descriptive name containing details about the experiments' parameters (more details in experiments.py and train.py)

If you have any problems feel free to contact me or open an issue.

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