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..
Fairness in Forecasting and Learning Linear Dynamical Systems
Authors: Quan Zhou, Jakub Marecek, Robert N. Shorten11134-11142
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate both the beneficial impact of fairness considerations on statistical performance and the encouraging effects of exploiting sparsity on run time. |
| Researcher Affiliation | Academia | Quan Zhou,1 Jakub Marecek,2 Robert Shorten 1,3 1 University College Dublin, Ireland 2 Czech Technical University in Prague, the Czech Republic 3 Imperial College London, UK EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available on-line at https://github.com/Quan Zhou/Fairness-in-Learning-of-LDS. |
| Open Datasets | Yes | Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate both the beneficial impact of fairness considerations on statistical performance and the encouraging effects of exploiting sparsity on run time. (Angwin et al. 2016) |
| Dataset Splits | No | The paper mentions 'training data' but does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts, or predefined splits) for reproducibility. |
| Hardware Specification | Yes | on a laptop equipped by Intel Core i7 8550U at 1.80 Ghz. |
| Software Dependencies | No | The paper mentions 'ncpol2sdpa of (Wittek 2015)' and 'sdpa of (Yamashita, Fujisawa, and Kojima 2003)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The three models (25)-(27) are applied in each experiment with λ of 1, 3, and 5, respectively, as chosen by iterating over integers 1 to 10. The initial states m(s) 0 of each subgroups are 5 and 7. We set the time window to be 20 across 3 trajectories in the advantaged subgroup and 2 in disadvantaged one, i.e., |T +| = 20, |I(a)| = 3 and |I(d)| = 2. |