Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Algorithmic Recourse for Long-Term Improvement
Authors: Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi
ICML 2025 | Venue PDF | 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. |