Distributionally Robust Recourse Action
Authors: Duy Nguyen, Ngoc Bui, Viet Anh Nguyen
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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) |