{"id":23325007,"url":"https://github.com/birkhoffg/rocoursenet","last_synced_at":"2025-04-07T06:22:45.526Z","repository":{"id":202632788,"uuid":"591111041","full_name":"BirkhoffG/RoCourseNet","owner":"BirkhoffG","description":"This is the official repository of the paper \"RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model\".","archived":false,"fork":false,"pushed_at":"2023-10-22T20:51:06.000Z","size":87671,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-13T10:36:30.245Z","etag":null,"topics":["counterfactual-explanations","explainable-ai","explanation","jax","jax-relax","recourse"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2206.00700","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BirkhoffG.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-01-20T00:01:52.000Z","updated_at":"2023-10-20T15:31:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"2e3eb904-cc91-45f9-975d-4145c0d3da57","html_url":"https://github.com/BirkhoffG/RoCourseNet","commit_stats":null,"previous_names":["birkhoffg/rocoursenet"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BirkhoffG%2FRoCourseNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BirkhoffG%2FRoCourseNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BirkhoffG%2FRoCourseNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BirkhoffG%2FRoCourseNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BirkhoffG","download_url":"https://codeload.github.com/BirkhoffG/RoCourseNet/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247601796,"owners_count":20964925,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["counterfactual-explanations","explainable-ai","explanation","jax","jax-relax","recourse"],"created_at":"2024-12-20T18:29:06.794Z","updated_at":"2025-04-07T06:22:45.505Z","avatar_url":"https://github.com/BirkhoffG.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model\n\n[![Arxiv](https://img.shields.io/badge/Arxiv-2206.00700-orange)](https://arxiv.org/pdf/2206.00700.pdf)\n[![DOI:10.1145/3583780.3615040](http://img.shields.io/badge/DOI-10.1145/3583780.3615040-B31B1B.svg)](https://dl.acm.org/doi/10.1145/3583780.3615040)\n\nThis repo contains code to reproduce our paper published at [CIKM 2023](https://arxiv.org/pdf/2206.00700.pdf).\n\nTo cite this paper:\n\n\n```bibtex\n@inproceedings{guo2023rocoursenet,\nauthor = {Guo, Hangzhi and Jia, Feiran and Chen, Jinghui and Squicciarini, Anna and Yadav, Amulya},\ntitle = {RoCourseNet: Robust Training of a Prediction Aware Recourse Model},\nyear = {2023},\nisbn = {9798400701245},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3583780.3615040},\ndoi = {10.1145/3583780.3615040},\nbooktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},\npages = {619–628},\nnumpages = {10},\nkeywords = {explainable artificial intelligence, adversarial machine learning, counterfactual explanation, algorithmic recourse, interpretability},\nlocation = {Birmingham, United Kingdom},\nseries = {CIKM '23}\n}\n```\n\n## Install\n\nThis project uses \n[jax-relax](https://github.com/BirkhoffG/ReLax/tree/master) (a fast and scalable recourse explanation library).\nThs library is highly scalable and extensible, which enables our experiments to be finished within 30 minutes.\nIn contrast, a pytorch implementation of RoCourseNet takes around 12 hours to run.\n\n```sh\npip install -e \".[dev]\" --upgrade\n```\n\n## Run Experiments\n\nRunning `scripts.experiment.py` with different arguments will reproduce results in our paper. For example,\n\n1. Train and Evaluate RoCourseNet on Loan Application Dataset:\n\n```sh\npython -m scripts.experiment.py -d loan\n```\n\n2. Train and Evaluate CounterNet on Loan Application Dataset:\n\n```sh\npython -m scripts.experiment.py -m CounterNet -d loan\n```\n\n3. Train and Evaluate ROAR on Loan Application Dataset:\n\n```sh\npython -m scripts.experiment.py -m ROAR -d loan\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbirkhoffg%2Frocoursenet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbirkhoffg%2Frocoursenet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbirkhoffg%2Frocoursenet/lists"}