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..
DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
Authors: Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By experiments on real datasets, we confirm the effectiveness of our method in comparison with existing methods for CE. |
| Researcher Affiliation | Collaboration | 1Hokkaido University 2Fujitsu Laboratories Ltd. 3Tokyo Institute of Technology |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper states, 'All codes were implemented in Python 3.6 with scikit-learn and IBM ILOG CPLEX v12.8.' However, it does not provide any concrete access information (e.g., URL or explicit statement of release) for the source code of their methodology. |
| Open Datasets | Yes | We used the FICO dataset (D = 23) [FICO et al., 2018] and german dataset (D = 61) [Dua and Graff, 2017] |
| Dataset Splits | No | The paper states: 'We randomly split each dataset into train (70%) and test (30%) instances,' but does not explicitly provide information about a separate validation dataset split. |
| Hardware Specification | Yes | All experiments were conducted on 64-bit Ubuntu 18.04.1 LTS with Intel Xeon E5-1620 v4 3.50GHz CPU and 62.8Gi B memory |
| Software Dependencies | Yes | All codes were implemented in Python 3.6 with scikit-learn and IBM ILOG CPLEX v12.8. |
| Experiment Setup | Yes | We randomly split each dataset into train (70%) and test (30%) instances, and trained ℓ2-regularized logistic regression (LR) classifiers and random forest (RF) classifiers with T = 100 decision trees on each training dataset, respectively. [...] We set λ = 1.0 for the FICO dataset and λ = 0.01 for the german dataset, respectively. |