ortools, scipy, pytorch, numpy, tqdm

1. mkdir ./data
2. The simulated paper bidding dataset can be downloaded from here. The data directory should be organized in the following way.

./data/raw_data/
       papers_dictionary.json
       reviewers_dictionary.json
       tensor_data.pl
       readme.txt

Use the following script to generate the features for every pair of paper and reviewer.

bash scripts/init_and_data_process_script.sh

This is an example script to train the linear regression model, evaluate its precision per reviewer / paper, and evaluate the assignment given by the regressor scores without any detection process.

bash scripts/assignment_script.sh

This is an example script to simulate white-box attack with colluding group size 5 and evaluate the success rate of this attack at each bin.

Comments for the arguments in the script:

• L: the number of additional colluding people.
• subsample_max: the parameter U introduced in the paper.
• K: the parameter that we only consider top K reviewer for each paper when we generate the final assignment.
• cheat_mode: the cheating mode (white_box / black_box).

bash scripts/attack_script.sh

This is an example script to evaluate our detection method against to the white-box attacking with colluding group size 5. The evaluation would run for different detection group sizes (1, 2, 3, 4, 5) together.

Comments for the arguments in the script:

• L_attack: the attack examples' additional colluding group size.
• Other arguments have the same meaning as the one in 3.

bash scripts/detect_tpr_script.sh

An example script to get the fpr when we set the colluding group size to be detected as 5.

Comments for the arguments in the script:

• L: the additional colluding group size set for detection.
• Other arguments have the same meaning as the one in 3 and 4.1.

bash scripts/detect_fpr_script.sh

This code corresponds to the following paper:

Ruihan Wu*, Chuan Guo*, Felix Wu, Rahul Kidambi, Laurens van der Maaten, and Kilian Q. Weinberger. Making Paper Reviewing Robust to Bid Manipulation Attacks. ICML 2021.

@article{wu2021making,
  title={Making Paper Reviewing Robust to Bid Manipulation Attacks},
  author={Wu, Ruihan and Guo, Chuan and Wu, Felix and Kidambi, Rahul and van der Maaten, Laurens and Weinberger, Kilian Q},
  journal={arXiv preprint arXiv:2102.06020},
  year={2021}
}

See the CONTRIBUTING file for how to help out.

This project is CC-BY-NC 4.0 licensed, as found in the LICENSE file.