Algorithmic Recourse for Long-Term Improvement
Authors: Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrated that our approaches could assign improvement-oriented actions to more instances than the existing methods. |
| Researcher Affiliation | Collaboration | 1Fujitsu Limited, Japan 2Institute of Science Tokyo, Japan 3The University of Electro-Communications, Japan. Correspondence to: Kentaro Kanamori <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 presents our algorithm for Problem 3.1 based on the CLB... Algorithm 2 presents our algorithm for Problem 3.1 based on the CBO with the Bo W forest. |
| Open Source Code | Yes | All the code was implemented in Python 3.10 and is available at https://github. com/kelicht/arlim. |
| Open Datasets | Yes | We used three real-world datasets: Credit (N = 30000, D = 13) (Yeh & hui Lien, 2009), Diabetes (N = 769, D = 8) (Dua & Graff, 2017), and COMPAS (N = 6167, D = 9) (Angwin et al., 2016). ... All the datasets used in our experiments are publicly available and do not contain any identifiable information or offensive content. |
| Dataset Splits | Yes | We randomly split the dataset S = {(xn, yn)}N n=1 into the training set Str, recourse set Sre, and test set Ste with a ratio of 2 : 1 : 1. |
| Hardware Specification | Yes | All the experiments were conducted on mac OS Sequoia with Apple M2 Ultra CPU and 128 GB memory. |
| Software Dependencies | Yes | All the code used in our experiments was implemented in Python 3.10 with scikit-learn 1.5.2. |
| Experiment Setup | Yes | For both Lin UCB and Bw OUCB, we set m = 10. We also set λ = 20.0 for Lin UCB and B = 50 for Bw OUCB, respectively. ... We used the ℓ1-norm a 1 as the cost function c and set ν = 1/D for computing the executing probability E. |