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..
Distributionally Robust Recourse Action
Authors: Duy Nguyen, Ngoc Bui, Viet Anh Nguyen
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments with both synthetic and three real-world datasets demonstrate the benefits of our proposed framework over state-of-the-art recourse methods. (Abstract) and We compare extensively the performance of our Di RRAc model (2) and Gaussian Di RRAc model (8) against four strong baselines: ROAR (Upadhyay et al., 2021), CEPM (Pawelczyk et al., 2020), AR (Ustun et al., 2019) and Wachter (Wachter et al., 2018). We conduct the experiments on three real-world datasets (German, SBA, Student). (Section 5) |
| Researcher Affiliation | Collaboration | Duy Nguyen1, Ngoc Bui1, Viet Anh Nguyen2 1Vin AI Research, Vietnam 2The Chinese University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 Projected gradient descent algorithm with backtracking line-search (Appendix E) |
| Open Source Code | Yes | Source code can be found at https://github.com/duykhuongnguyen/Di RRAc. (Appendix A.1) |
| Open Datasets | Yes | We use three real-world datasets which capture different data distribution shifts (Dua & Graff, 2017): (i) the German credit dataset, which captures a correction shift. (ii) the Small Business Administration (SBA) dataset, which captures a temporal shift. (iii) the Student performance dataset, which captures a geospatial shift. (Section 5) and For the German credit dataset from the UCI repository... (Appendix A.1) |
| Dataset Splits | No | The paper states We split 80% of the original dataset and train a logistic classifier but does not explicitly mention a separate validation split for hyperparameter tuning or early stopping, or specify a cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or cloud computing specifications) used to run the experiments. |
| Software Dependencies | Yes | off-the-shelf solvers such as GUROBI Gurobi Optimization, LLC (2021) or Mosek (MOSEK Ap S, 2019). (Section 3.3) and MOSEK Optimizer API for Python 9.2.10, 2019. (Reference section, MOSEK Ap S entry) |
| Experiment Setup | Yes | The detailed settings are provided in Table 4. Table 4: Parameters for the experiments with real-world data in Table 1. Parameters Values K 1 δadd 1.0 bp [1] ρ [0.1] λ 0.7 ζ 1 (Appendix A.2) |