This repository contains the supporting code for the paper:

Staib, Matthew and Jegelka, Stefanie. Robust Budget Allocation via Continuous Submodular Functions. In Proceedings of the 34th International Conference on Machine Learning, 2017.

@inproceedings{staib2017robust,
 author = {Staib, Matthew and Jegelka, Stefanie},
 title = {Robust Budget Allocation via Continuous Submodular Functions},
 booktitle = {Proceedings of the 34th International Conference on Machine Learning},
 year = {2017}
}

• TFOCS
• MOSEK (for the first-order comparison experiments)
• Yahoo! Webscope dataset ydata-ysm-advertiser-bids-v1.0 (for some experiments)

1. First run addpaths to add subdirectories to the path
2. Run mexMake to build mex code
3. If you plan to use the Yahoo! data, update line 75 of experiments/get_yahoo_problem_func.m to point to the location of this dataset on your system. (the default is ../Webscope_A1/ydata-ysm-advertiser-bids-v1_0.txt)

At this point you can run the experiments from the paper, or try your own by passing different parameter combinations into experiments/run_all_params.m. Plotting utilities are available for some specific experiments already:

• experiments/rho_uniqueness_experiment.m produces Figure 1 from the paper, which tests the uniqueness of values in the vector rho which we threshold in order to recover solutions to the constrained SFM problem.
• After running the synthetic experiments (experiments/all_experiments.m), and pointing it to the corresponding output folder, experiments/synthetic_influence_comparison_plots.m will produce Figure 2 from the paper.
• After running the Yahoo! experiments (experiments/all_yahoo_experiments.m) and pointing it to the corresponding output folder, experiments/plot_yahoo_convergence.m produces Figure 3 from the paper.
• After running the Yahoo! experiments (experiments/all_yahoo_experiments.m) and pointing it to the output file from a particular run, experiments/gradient_descent_yahoo_compare.m produces Figure 4 from the paper (requires MOSEK).

I am still in the process of better documenting parts of the code (this is a living repo), but for now, enjoy!