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..
Wasserstein Distributionally Robust Inverse Multiobjective Optimization
Authors: Chaosheng Dong, Bo Zeng5914-5921
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of our method on both a synthetic multiobjective quadratic program and a real world portfolio optimization problem. Experiments In this section, we provide an MQP and a portfolio optimization problem to illustrate the performance of Algorithm 1. |
| Researcher Affiliation | Collaboration | Chaosheng Dong1*, Bo Zeng2 1Amazon 2University of Pittsburgh EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Wasserstein Distributionally Robust IMOP |
| Open Source Code | No | The paper states 'All the algorithms are programmed with Julia (Bezanson et al. 2017)' but does not provide a link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | No | The paper describes how synthetic data and real-world case study data are generated/derived ('We ο¬rst compute Pareto optimal solutions {xi}i [N] by solving WP with weight samples {wi}i [N] that are uniformly chosen from W2. Next, the noisy decision yi is obtained by adding noise to xi for each i [N].' and 'The dataset is derived from monthly total returns of 30 stocks from a blue-chip index...'). It refers to 'supplementary material' for 'true expected returns and true return covariance matrix', but does not provide a concrete link or citation to an openly accessible public dataset with author attribution or a repository for direct download. |
| Dataset Splits | Yes | Here, we use an independent validation set that consists of 105 noisy decisions generated in the same way as the training data to compute the prediction error. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or memory) are provided. |
| Software Dependencies | No | The paper mentions 'Gurobi' and 'Julia' but does not specify their version numbers. It also refers to 'Baron (Sahinidis 1996)' without a version. |
| Experiment Setup | Yes | K = 6 weights from W2 are evenly sampled. The radius Ο΅ of the Wasserstein ambiguity set is selected from the set {10 4, 10 3, 10 2, 10 1, 1}. The stopping criteria Ξ΄ is set to be 0.1. |