Adaptive Sampling for Stochastic Risk-Averse Learning

This package is the companion of the paper `Adaptive Sampling for Stochastic Risk-Averse Learning' by Sebastian Curi, Kfir. Y. Levy, Stefanie Jegelka, Andreas Krause.

To install the package run:

$ pip install .
$ pip install .[test]

To check that everything runs fine run the testing script.

$ bash scripts/test_code.sh

To run an experiment run

$ python adacvar/run.py

To see the arguments run

$ python adacvar/run.py --help 

To reproduce experiments run from the main directory the following commands.

$ python experiments/run_task.py classification 
$ python experiments/run_task.py regression --shifts None
$ python experiments/run_task.py mnist --num-threads 4
$ python experiments/run_task.py fashion-mnist --num-threads 4
$ python experiments/run_task.py cifar-10 --num-threads 6 --use-gpu
$ python experiments/run_task.py trade-off --num-threads 6 --use-gpu
$ python experiments/classification-shift --job run --alpha 0.1 --shifts train test both double
$ python experiments/classification-upsample --job run --alpha 0.1 --shifts train test both double

To post-process the experiments run.

$ python experiments/run_task.py classification --job post-process
$ python experiments/run_task.py regression --job post-process --shifts None
$ python experiments/run_task.py vision --job post-process --datasets mnist-augmented fashion-mnist-augmented cifar-10-augmented
$ python experiments/datashift_process_runs.py

To plot the experiments run

$ python experiments/run_task.py classification --job plot
$ python experiments/run_task.py regression --job plot --shifts None
$ python experiments/run_task.py vision --job plot --shifts train --datasets mnist-augmented fashion-mnist-augmented --alphas 0.01 0.1
$ python experiments/

Citation:

If you use adacvar in your research please use the following BibTeX entry:

@article{curi2020adaptive,
  title={Adaptive sampling for stochastic risk-averse learning},
  author={Curi, Sebastian and Levy, Kfir Y and Jegelka, Stefanie and Krause, Andreas},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}